Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes

A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

Estimating bottom properties with a vector sensor array during MakaiEx 2005

Nowadays, vector sensors which measure both acoustic pressure and particle velocity begin to be available in underwater acoustic systems, normally configured as vector sensor arrays (VSA). The spatial filtering capabilities of a VSA can be used, with advantage over traditional pressure only hydrophone arrays, for estimating acoustic field directionality as well as arrival times and spectral content, which could open up the possibility for its use in bottom properties' estimation. An additional motivation for this work is to test the possibility of using high frequency probe signals (say above 2 kHz) for reducing size and cost of actual sub bottom profilers and current geoacoustic inversion methods. This work studies the bottom related structure of the VSA acquired signals, regarding the emitted signal waveform, frequency band and source-receiver geometry in order to estimate bottom properties, specially bottom reflection coefficient characteristics. Such a system was used during the Makai 2005 experiment, off Kauai I., Hawai (USA) to receive precoded signals in a broad frequency band from 8 up to 14 kHz. The agreement between the observed and the modelled acoustic data is discussed and preliminary results on the bottom reflection estimation are presented.

Source localization with vector sensor array during the makai experiment

Vector sensors measure both the acoustic pressure and the three components of particle velocity. Because of this, a vector sensor array (VSA) has the advantage of being able to provide substantially higher directivity with a much smaller aperture than an array of traditional scalar (pressure only) hydrophones. Although several, most of them theoretic, works were published from early nineties, only in the last years due to improvements and availability of vector sensor technology, the interest on field experiments with VSA increased in the scientific community. During the Makai Experiment, that took place off the coast of Kauai I., Hawaii, in September 2005, real data were collected with a 4 element vertical VSA. These data will be discussed in the present paper. The acoustic signals were emitted from a near source (low frequency ship noise) and two high frequency controlled acoustic sources located within a range of 2km from the VSA. The advantages of the VSA over traditional scalar hydrophone arrays in source localization will be addressed using conventional beamforming.

Simulation of the magnetic induction vector of a magnetic core to be used in FFC NMR relaxometry

This paper is a contribution for the assessment and comparison of magnet properties based on magnetic field characteristics particularly concerning the magnetic induction uniformity in the air gaps. For this aim, a solver was developed and implemented to determine the magnetic field of a magnetic core to be used in Fast Field Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry. The electromagnetic field computation is based on a 2D finite-element method (FEM) using both the scalar and the vector potential formulation. Results for the magnetic field lines and the magnetic induction vector in the air gap are presented. The target magnetic induction is 0.2 T, which is a typical requirement of the FFC NMR technique, which can be achieved with a magnetic core based on permanent magnets or coils. In addition, this application requires high magnetic induction uniformity. To achieve this goal, a solution including superconducting pieces is analyzed. Results are compared with a different FEM program.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Effect of age on female choice in the field cricket, Gryllus interger

Female choice is an important element of sexual selection that may vary among females of the same species. Few researchers have investigated the causes of variation in selectivity with respect to potential mates and overall level of motivation toward a stimulus source representative of a mate. This study demonstrates that female age may be one cause of variation in female choice. Females of different ages may have different mate preferences. As females age, they have less time left to reproduce, and their residual reproductive value decreases. This should correspond to a higher reproductive effort which may be represented as increased motivation and/or decreased selectivity. The effect of age on mate choice in Gryllus integer was investigated by using a non-compensating treadmill, called the Kugel, to measure female phonotaxis. Artificially generated male calling songs of varying pulse rates were broadcast in either a singlestimulus or a three-stimulus experimental design. The pulse rates used in the calling song stimuli were 70, 64, 76, 55 and 85 pulses per second. These corresponded to the documented mean pulse rate for the species at the experimental temperature, one standard deviation below and above the mean, and 2.5 standard deviations below and above the mean, respectively. Test females were either 11-14 days or 25-28 days post-ecdysis. Trials usually were conducted two to seven hours into the scotophase. In the single-stimulus experiment, females were presented with stimuli with only one pulse rate. Older females achieved higher vector scores than younger females, indicating that older females are more motivated to mate. Both groups showed little phonotactic response towards 55 or 85 pIs, both of which lie outside the natural range of G. integer calling song at the experimental temperature. Neither group discriminated among the three pulse rates that fell within the natural range of calling song. In the three-stimulus experiment, females were presented with stimuli with one of three pulse rates, 64, 70 or 76 pIs, In alternation. Both age groups had reduced responsiveness in this experiment, perhaps due to an increase in perceived male density. Additionally, younger females responded significantly more to 64 and 70 pIs than to the higher pulse rate, indicating that they are selective with respect to mate choice. Older females did not discriminate among the three pulse rates. Therefore, it was concluded that selectivity decreases with age. A further study was conducted to determine that these effects were due to age and not due to the differing periods without a mating between the two age groups. Again, stimuli were presented in a three-stimulus experimental design. Age was held constant at 28 days and time since last mating varied from 11 to 25 days. Females varyIng in time since last mating did not differ in their responses to the calling song pulse rates. This indicated that the increased motivation and decreased selectivity exhibited In the initial experiments were due to age and not to time without a mating. Neither time of trial nor female weight had an effect upon female phonotaxis. Data are discussed in terms of mate choice, residual reproductive value, and costs of choice.

The effect of diet and parasite load on female phonotaxis in the Texas field cricket, Gryllus integer

Female crickets respond selectively to variations in species-specific male calling songs. This selectivity has been shown to be age-dependent; older females are less choosy. However, female quality should also affect female selectivity. The effect of female quality on mate choice was examined in Gryllus integer by comparing the phonotactic responses of females on different diets and with different parasite loads to various synthetic models of conspecific calling song. Test females were virgin, 11-14 days old, and had been maintained on one of five diets varying in protein and fat content. Phonotaxis was quantified using a non-compensating Kugel treadmill which generates vector scores incorporating the speed and direction of movement of each female. Test females were presented with four calling song models which differed in pulse rate, but were still within the natural range of the species for the experimental temperature. After testing, females were dissected and the number of gregarine parasites within the digestive tract counted. There were no significant effects of either diet or parasitism on female motivation to mate although the combined effects of these variables seem to have an effect with no apparent trend. Control females did not discriminate among song types, but there was a trend of female preferences for lower pulse rates which are closest to the mean pulse rate for the species. Heavily parasitized females did not discriminate among pulse rates altho~gh there was a similar trend of high vector scores for low pulse rates. Diet, however, affected selectivity with poorly-fed females showing significantly high vector scores for pulse rates near the species mean. Such findings raise interesting questions about energy allocation and costs and risks of phonotaxis and mate choice in acoustic Orthoptera. These results are discussed in terms of sexual selection and female mate choice.

Effect of Different Forms of Poly o-Toluidine /Poly Vinyl Chloride Composites on Dielectric Propertiesin the Microwave Field

In this paper we report the preparation and dielectric properties of poly o-toluidine:poly vinyl chloride composites in pellet and film forms. The composites were prepared using ammonium persulfate initiator and HCl dopant. The characterization is done by TGA and DSC. The dielectric properties including dielectric loss, conductivity, dielectric constant, dielectric heating coefficient, absorption coefficient, and penetration depth were studied in the microwave field. An HP8510 vector network analyzer with rectangular cavity resonator was used for the study. Sbands (2-4 GHz), C band (5-8 GHz), and X band (8-12 GHz) frequencies were used in the microwave field. Comparisons between the pellet and film forms of composites were also included. The result shows that the dielectric properties in the microwave field are dependent on the frequency and on the method of preparation.

Field Equilibrium Finite Elements for Structural Analysis

Many finite elements used in structural analysis possess deficiencies like shear locking, incompressibility locking, poor stress predictions within the element domain, violent stress oscillation, poor convergence etc. An approach that can probably overcome many of these problems would be to consider elements in which the assumed displacement functions satisfy the equations of stress field equilibrium. In this method, the finite element will not only have nodal equilibrium of forces, but also have inner stress field equilibrium. The displacement interpolation functions inside each individual element are truncated polynomial solutions of differential equations. Such elements are likely to give better solutions than the existing elements.In this thesis, a new family of finite elements in which the assumed displacement function satisfies the differential equations of stress field equilibrium is proposed. A general procedure for constructing the displacement functions and use of these functions in the generation of elemental stiffness matrices has been developed. The approach to develop field equilibrium elements is quite general and various elements to analyse different types of structures can be formulated from corresponding stress field equilibrium equations. Using this procedure, a nine node quadrilateral element SFCNQ for plane stress analysis, a sixteen node solid element SFCSS for three dimensional stress analysis and a four node quadrilateral element SFCFP for plate bending problems have been formulated.For implementing these elements, computer programs based on modular concepts have been developed. Numerical investigations on the performance of these elements have been carried out through standard test problems for validation purpose. Comparisons involving theoretical closed form solutions as well as results obtained with existing finite elements have also been made. It is found that the new elements perform well in all the situations considered. Solutions in all the cases converge correctly to the exact values. In many cases, convergence is faster when compared with other existing finite elements. The behaviour of field consistent elements would definitely generate a great deal of interest amongst the users of the finite elements.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Vector-Based Integration of Local and Long-Range Information in Visual Cortex

Integration of inputs by cortical neurons provides the basis for the complex information processing performed in the cerebral cortex. Here, we propose a new analytic framework for understanding integration within cortical neuronal receptive fields. Based on the synaptic organization of cortex, we argue that neuronal integration is a systems--level process better studied in terms of local cortical circuitry than at the level of single neurons, and we present a method for constructing self-contained modules which capture (nonlinear) local circuit interactions. In this framework, receptive field elements naturally have dual (rather than the traditional unitary influence since they drive both excitatory and inhibitory cortical neurons. This vector-based analysis, in contrast to scalarsapproaches, greatly simplifies integration by permitting linear summation of inputs from both "classical" and "extraclassical" receptive field regions. We illustrate this by explaining two complex visual cortical phenomena, which are incompatible with scalar notions of neuronal integration.

Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik"s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.

An Equivalence Between Sparse Approximation and Support Vector Machines

In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, 1995) and Olshausen and Field (1996). Then we focus on two specific (approximate) implementations of SRM and Sparse Approximation, which have been used to solve the problem of function approximation. For SRM we consider the Support Vector Machine technique proposed by V. Vapnik and his team at AT&T Bell Labs, and for Sparse Approximation we consider a modification of the Basis Pursuit De-Noising algorithm proposed by Chen, Donoho and Saunders (1995). We show that, under certain conditions, these two techniques are equivalent: they give the same solution and they require the solution of the same quadratic programming problem.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.