Efficient Compression and Encryption for Digital Data Transmission

We live in a digital era in which communication is largely based on the exchange of digital information on data networks. Communication is often pictured as a sender that transmits a digital file to a receiver. This file travels from a source to a destination and, to have a quick and immediate communication, we need an encoding strategy that should be efficient and easy yet secure. This communication could be based on a layout articulated in two operations that are heterogeneous and in some case conflicting but that are needed to be applied to the original file to have efficiency and security. These two operations are data compression and encryption. The aim of this work is to study the combination of compression and encryption techniques in digital documents. In this paper we will test the combinations of some of the state-of-the-art compression and cryptography techniques in various kinds of digital data

Multiband and Lossless Compression of Hyperspectral Images

Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images

Multilevel Variable-Block Schur-Complement-Based Preconditioning for the Implicit Solution of the Reynolds- Averaged Navier-Stokes Equations Using Unstructured Grids

Implicit methods based on the Newton’s rootfinding algorithm are receiving an increasing attention for the solution of complex Computational Fluid Dynamics (CFD) applications due to their potential to converge in a very small number of iterations. This approach requires fast convergence acceleration techniques in order to compete with other conventional solvers, such as those based on artificial dissipation or upwind schemes, in terms of CPU time. In this chapter, we describe a multilevel variable-block Schur-complement-based preconditioning for the implicit solution of the Reynolds-averaged Navier-Stokes equations using unstructured grids on distributed-memory parallel computers. The proposed solver detects automatically exact or approximate dense structures in the linear system arising from the discretization, and exploits this information to enhance the robustness and improve the scalability of the block factorization. A complete study of the numerical and parallel performance of the solver is presented for the analysis of turbulent Navier-Stokes equations on a suite of three-dimensional test cases

Copyright Protection for Digital Images on Portable Devices

The astonishing rapid diffusion of portable devices (i.e. smartphones, tablets, etc.) has had a big, and often positive, impact on our every-day life. These devices have new advanced features developed specifically because of user demand. For example, it is now possible to publish directly the pictures obtained by means of the internal camera of a smartphone on our social network accounts, or on an image hosting service. It is therefore important to have tools, on the portable devices, that can prove the ownership of the pictures and to use them before publishing images. Digital watermarking techniques are commonly used for the copyright protection of images and videos. We have developed a tool for portable devices based on the Android OS that allows the embedding of a digital visible or invisible watermark into a digital image

Restarted Hessenberg method for solving shifted nonsymmetric linear systems

It is known that the restarted full orthogonalization method (FOM) outperforms the restarted generalized minimum residual (GMRES) method in several circumstances for solving shifted linear systems when the shifts are handled simultaneously. Many variants of them have been proposed to enhance their performance. We show that another restarted method, the restarted Hessenberg method [M. Heyouni, M\'ethode de Hessenberg G\'en\'eralis\'ee et Applications, Ph.D. Thesis, Universit\'e des Sciences et Technologies de Lille, France, 1996] based on Hessenberg procedure, can effectively be employed, which can provide accelerating convergence rate with respect to the number of restarts. Theoretical analysis shows that the new residual of shifted restarted Hessenberg method is still collinear with each other. In these cases where the proposed algorithm needs less enough CPU time elapsed to converge than the earlier established restarted shifted FOM, weighted restarted shifted FOM, and some other popular shifted iterative solvers based on the short-term vector recurrence, as shown via extensive numerical experiments involving the recent popular applications of handling the time fractional differential equations.Comment: 19 pages, 7 tables. Some corrections for updating the reference