Search Results (10)

In an AI-immersing age, scholars look for new possibilities of employing AI technology to their fields, and how to strengthen security and protect privacy is no exception. In a coverless data hiding domain, the embedding capacity of an image generally depends on the size of a chosen database. Therefore, choosing a suitable database is a critical issue in coverless data hiding. A novel coverless data hiding approach is proposed by applying deep learning models to generate texture-like cover images or code images. These code images are then used to construct steganographic images to transmit covert messages. Effective mapping tables between code images in the database and hash sequences are established during the process. The cover images generated by a two-dimensional fractional Brownian motion (2D FBM) are simply called fractional Brownian images (FBIs). The only parameter, the Hurst exponent, of the 2D FBM determines the patterns of these cover images, and the seeds of a random number generator determine the various appearances of a pattern. Through the 2D FBM, we can easily generate as many FBIs of multifarious sizes, patterns, and appearances as possible whenever and wherever. In the paper, a deep learning model is treated as a secret key selecting qualified FBIs as code images to encode corresponding hash sequences. Both different seeds and different deep learning models can pick out diverse qualified FBIs. The proposed coverless data hiding scheme is effective when the amount of secret data is limited. The experimental results show that our proposed approach is more reliable, efficient, and of higher embedding capacity, compared to other coverless data hiding methods. Full article

The fractal dimension (D) is a very useful indicator for recognizing images. The fractal dimension increases as the pattern of an image becomes rougher. Therefore, images are frequently described as certain models of fractal geometry. Among the models, two-dimensional fractional Brownian motion (2D FBM) is commonly used because it has specific physical meaning and only contains the finite-valued parameter (a real value from 0 to 1) of the Hurst exponent (H). More usefully, H and D possess the relation of D = 3 − H. The accuracy of the maximum likelihood estimator (MLE) is the best among estimators, but its efficiency is appreciably low. Lately, an efficient MLE for the Hurst exponent was produced to greatly improve its efficiency, but it still incurs much higher computational costs. Therefore, in the paper, we put forward a deep-learning estimator through classification models. The trained deep-learning models for images of 2D FBM not only incur smaller computational costs but also provide smaller mean-squared errors than the efficient MLE, except for size 32 × 32 × 1. In particular, the computational times of the efficient MLE are up to 129, 3090, and 156248 times those of our proposed simple model for sizes 32 × 32 × 1, 64 × 64 × 1, and 128 × 128 × 1. Full article

Marine and terrestrial natural surfaces exhibit statistical scale invariance properties that are well modelled by fractional Brownian motion (fBm), two-dimensional random processes. Accordingly, for microwave remote sensing applications it is useful to evaluate the normalized radar cross section (NRCS) of fBm surfaces. This task has been accomplished in the past by using either the Kirchhoff approximation (KA) or the small perturbation method (SPM). However, KA and SPM have rather limited ranges of application in terms of surface roughness and incidence angle: a wider range of application is achieved by the small slope approximation (SSA), more recently developed, but the latter has not been applied yet to fBm surfaces. In this paper, the first-order SSA (SSA-1) is applied to the evaluation of scattering from fBm surfaces obtaining an analytical formulation of their NRCS. It is then shown that the obtained SSA-1 expression reduces to the KA and SPM ones at near-specular and far-from-specular scattering directions, respectively. Finally, the results of the proposed method are compared to experimental data available in the literature. Full article

Two-dimensional fractional Brownian motion (2D FBM) is an effective model for describing natural scenes and medical images. Essentially, it is characterized by the Hurst exponent (H) or its corresponding fractal dimension (D). For optimal accuracy, we can use the maximum likelihood estimator (MLE) to compute the value. However, its computational cost is much higher than other low-accuracy estimators. Therefore, we propose a feasible deep-learning model and find out some promising pretrained models to classify the Hurst exponent efficiently and effectively. For evaluating the efficacy of deep learning models, two types of 2D FBM images were generated—11 classes and 21 classes of Hurst exponents. For comparison, we first used the efficient MLE to estimate the Hurst exponent of each image and then classified them through machine learning models. On the other hand, we used deep learning models to train and classify all images. Experimental results show that our proposed model and some pretrained models are much higher in accuracy than machine learning models for estimates from the efficient MLE. When applied, deep learning models take much lower computational time than the efficient MLE. Therefore, for accuracy and efficiency, we can use deep learning models to replace the role of the efficient MLE in the future. Full article

In the present framework, an analysis on nanofluid magneto-transport phenomena over an extending cylinder influenced by gyrotactic behavior of algal suspension, is made using the Cattaneo–Christov heat flux (non-Fourier) and mass flux (non-Fick’s) concept in modified Buongiorno’s model. Two dimensional incompressible MHD hybrid nanofluid which comprises chemically reactive hybrid nanomaterials (Ag-MgO NPs) and Stefan blowing effect along with multiple slips is considered. The experimental correlations with their dependency on initial nanoparticle volume fraction are used for viscosity and thermal conductivity of nanofluids. Similarity transformation is used to convert the governing PDE’s into non-linear ODE’s along with boundary conditions, which are solved using the Galerkin Finite Element Method (GFEM). The mesh independent test with different boundary layer thickness (${\xi }_{\infty }$) has been conducted by taking both linear and quadratic shape functions to achieve a optimal desired value. The results are calculated for a realistic range of physical parameters. The validation of FEM results shows an excellent correlation with MATLAB bvp5c subroutine. The warmth exhibitions are assessed through modified version of Buongiorno’s model which effectively reflects the significant highlights of Stefan blowing, slip, curvature, free stream, thermophoresis, Brownian motion and bio-convection parameters. The present study in cylindrical domain is relevant to novel microbial fuel cell technologies utilizing hybrid nanoparticles and concept of Stefan blowing with bioconvection phenomena. Full article

Fractional-order time and space derivatives are one way to augment the classical diffusion equation so that it accounts for the non-Gaussian processes often observed in heterogeneous materials. Two-dimensional phase diagrams—plots whose axes represent the fractional derivative order—typically display: (i) points corresponding to distinct diffusion propagators (Gaussian, Cauchy), (ii) lines along which specific stochastic models apply (Lévy process, subordinated Brownian motion), and (iii) regions of super- and sub-diffusion where the mean squared displacement grows faster or slower than a linear function of diffusion time (i.e., anomalous diffusion). Three-dimensional phase cubes are a convenient way to classify models of anomalous diffusion (continuous time random walk, fractional motion, fractal derivative). Specifically, each type of fractional derivative when combined with an assumed power law behavior in the diffusion coefficient renders a characteristic picture of the underlying particle motion. The corresponding phase diagrams, like pages in a sketch book, provide a portfolio of representations of anomalous diffusion. The anomalous diffusion phase cube employs lines of super-diffusion (Lévy process), sub-diffusion (subordinated Brownian motion), and quasi-Gaussian behavior to stitch together equivalent regions. Full article

Numeric simulations are performed for a comparative study of magnetohydrodynamic (MHD) rotational flow of hybrid nanofluids (MoS₂-Ag/ethyleneglycol-water (50–50%) and MoS₂-Go/ethyleneglycol-water (50–50%)) over a horizontally elongated plane sheet. The principal objective is concerned with the enhancement of thermal transportation. The three-dimensional formulation governing the conservation of mass, momentum, energy, and concentration is transmuted into two-dimensional partial differentiation by employing similarity transforms. The resulting set of equations (PDEs) is then solved by variational finite element procedure coded in Matlab script. An intensive computational run is carried out for suitable ranges of the particular quantities of influence. The primary velocity component decreases monotonically and the magnitude of secondary velocity component diminishes significantly when magnetic parameter, rotational parameter, and unsteadiness parameter are incremented. Both the primary and secondary velocities are smaller in values for the hybrid phase Ag-MoS₂ than that of hybrid phase Go-MoS₂ but the nanoparticle concentration and temperature are higher for hybrid phase Ag-MoS₂. The increased values of parameters for thermophoresis, Brownian motion, shape factor, and volume fraction of ${\varphi }_2$ made significant improvement in the temperature of the two phases of nano liquids. Results are also computed for the coefficients of skin friction(x, y-directions), Nusselt number, and Sherwood number. The present findings manifest reasonable comparison to their existing counterparts. Some of the practical engineering applications of the present analysis may be found in high-temperature nanomaterial processing technology, crystal growing, extrusion processes, manufacturing and rolling of polymer sheets, academic research, lubrication processes, and polymer industry. Full article

The mixed convection two-phase flow and heat transfer of nanofluids were addressed within a wavy wall enclosure containing a solid rotating cylinder. The annulus area between the cylinder and the enclosure was filled with water-alumina nanofluid. Buongiorno’s model was applied to assess the local distribution of nanoparticles in the host fluid. The governing equations for the mass conservation of nanofluid, nanoparticles, and energy conservation in the nanofluid and the rotating cylinder were carried out and converted to a non-dimensional pattern. The finite element technique was utilized for solving the equations numerically. The influence of the undulations, Richardson number, the volume fraction of nanoparticles, rotation direction, and the size of the rotating cylinder were examined on the streamlines, heat transfer rate, and the distribution of nanoparticles. The Brownian motion and thermophoresis forces induced a notable distribution of nanoparticles in the enclosure. The best heat transfer rate was observed for 3% volume fraction of alumina nanoparticles. The optimum number of undulations for the best heat transfer rate depends on the rotation direction of the cylinder. In the case of counterclockwise rotation of the cylinder, a single undulation leads to the best heat transfer rate for nanoparticles volume fraction about 3%. The increase of undulations number traps more nanoparticles near the wavy surface. Full article

It is necessary to control the temperature of solar cells for enhancing efficiency with increasing concentrations of multiple photovoltaic systems. A heterogeneous two-phase model was established after considering the interacting between temperature, viscosity, the flow of nanofluid, and the motion of nanoparticles in the nanofluid, in order to study the microchannel heat sink (MCHS) using Al₂O₃-water nanofluid as coolant in the photovoltaic system. Numerical simulations were carried out to investigate the thermal performance of MCHS with a series of trapezoidal grooves. The numerical results showed us that, (1) better thermal performance of MCSH using nanofluid can be achieved from a heterogeneous two-phase model than that from single-phase model; (2) The effects of flow field, volume fraction, nanoparticle size on the heat transfer enhancement in MCHS were interpreted by a non-dimensional parameter N_BT (i.e., ratio of Brownian diffusion and thermophoretic diffusion). In addition, the geometrical parameters of MCHS and the physical parameters of the nanofluid were optimized. This can provide a sound foundation for the design of MCHS. Full article

The origin of the long-range memory in non-equilibrium systems is still an open problem as the phenomenon can be reproduced using models based on Markov processes. In these cases, the notion of spurious memory is introduced. A good example of Markov processes with spurious memory is a stochastic process driven by a non-linear stochastic differential equation (SDE). This example is at odds with models built using fractional Brownian motion (fBm). We analyze the differences between these two cases seeking to establish possible empirical tests of the origin of the observed long-range memory. We investigate probability density functions (PDFs) of burst and inter-burst duration in numerically-obtained time series and compare with the results of fBm. Our analysis confirms that the characteristic feature of the processes described by a one-dimensional SDE is the power-law exponent $3/2$ of the burst or inter-burst duration PDF. This property of stochastic processes might be used to detect spurious memory in various non-equilibrium systems, where observed macroscopic behavior can be derived from the imitative interactions of agents. Full article