Intelligent OFDM telecommunication system. Part 1. Model of complex and quaternion systems

In this paper, we aim to investigate the superiority and practicability of many-parameter transforms (MPTs) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication systems based on complex and quaternion MPTs. The new systems use inverse MPT (IMPT) for modulation at the transmitter and MPT for demodulation at the receiver. The purpose of employing the MPT is to improve: 1) the PHY-LS of wireless transmissions against to the wide-band anti-jamming and anti-eavesdropping communication; 2) the bit error rate (BER) performance with respect to the conventional OFDM-TCS; 3) the peak to average power ratio (PAPR). Each MPT depends on finite set of independent parameters (angles). When parameters are changed, many-parametric transform is also changed taking form of a set known (and unknown) orthogonal (or unitary) transforms. For this reason, the concrete values of parameters are specific "key" for entry into OFDM-TCS. Vector of parameters belong to multi-dimension torus space. Scanning of this space for find out the "key" (the concrete values of parameters) is hard problem. MPT has the form of the product of the Jacobi rotation matrixes and it describes a fast algorithm for MPT. The main advantage of using MPT in OFDM TCS is that it is a very flexible anti-eavesdropping and anti-jamming Intelligent OFDM TCS. To the best of our knowledge, this is the first work that utilizes the MPT theory to facilitate the PHY-LS through parameterization of unitary transforms. © 2019 IOP Publishing Ltd. All rights reserved

Improving files availability for BitTorrent using a diffusion model

The BitTorrent mechanism effectively spreads file fragments by copying the rarest fragments first. We propose to apply a mathematical model for the diffusion of fragments on a P2P in order to take into account both the effects of peer distances and the changing availability of peers while time goes on. Moreover, we manage to provide a forecast on the availability of a torrent thanks to a neural network that models the behaviour of peers on the P2P system. The combination of the mathematical model and the neural network provides a solution for choosing file fragments that need to be copied first, in order to ensure their continuous availability, counteracting possible disconnections by some peers

Performance of the wavelet-transform-neural network based receiver for DPIM in diffuse indoor optical wireless links in presence of artificial light interference

Artificial neural network (ANN) has application in communication engineering in diverse areas such as channel equalization, channel modeling, error control code because of its capability of nonlinear processing, adaptability, and parallel processing. On the other hand, wavelet transform (WT) with both the time and the frequency resolution provides the exact representation of signal in both domains. Applying these signal processing tools for channel compensation and noise reduction can provide an enhanced performance compared to the traditional tools. In this paper, the slot error rate (SER) performance of digital pulse interval modulation (DPIM) in diffuse indoor optical wireless (OW) links subjected to the artificial light interference (ALI) is reported with new receiver structure based on the discrete WT (DWT) and ANN. Simulation results show that the DWT-ANN based receiver is very effective in reducing the effect of multipath induced inter-symbol interference (ISI) and ALI

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Linear Scaling Solution of the Coulomb problem using wavelets

The Coulomb problem for continuous charge distributions is a central problem in physics. Powerful methods, that scale linearly with system size and that allow us to use different resolutions in different regions of space are therefore highly desirable. Using wavelet based Multi Resolution Analysis we derive for the first time a method which has these properties. The power and accuracy of the method is illustrated by applying it to the calculation of of the electrostatic potential of a full three-dimensional all-electron Uranium dimer

Time frequency analysis in terahertz pulsed imaging

Recent advances in laser and electro-optical technologies have made the previously under-utilized terahertz frequency band of the electromagnetic spectrum accessible for practical imaging. Applications are emerging, notably in the biomedical domain. In this chapter the technique of terahertz pulsed imaging is introduced in some detail. The need for special computer vision methods, which arises from the use of pulses of radiation and the acquisition of a time series at each pixel, is described. The nature of the data is a challenge since we are interested not only in the frequency composition of the pulses, but also how these differ for different parts of the pulse. Conventional and short-time Fourier transforms and wavelets were used in preliminary experiments on the analysis of terahertz pulsed imaging data. Measurements of refractive index and absorption coefficient were compared, wavelet compression assessed and image classification by multidimensional clustering techniques demonstrated. It is shown that the timefrequency methods perform as well as conventional analysis for determining material properties. Wavelet compression gave results that were robust through compressions that used only 20% of the wavelet coefficients. It is concluded that the time-frequency methods hold great promise for optimizing the extraction of the spectroscopic information contained in each terahertz pulse, for the analysis of more complex signals comprising multiple pulses or from recently introduced acquisition techniques

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Wavelet transforms and their applications to MHD and plasma turbulence: a review

Wavelet analysis and compression tools are reviewed and different applications to study MHD and plasma turbulence are presented. We introduce the continuous and the orthogonal wavelet transform and detail several statistical diagnostics based on the wavelet coefficients. We then show how to extract coherent structures out of fully developed turbulent flows using wavelet-based denoising. Finally some multiscale numerical simulation schemes using wavelets are described. Several examples for analyzing, compressing and computing one, two and three dimensional turbulent MHD or plasma flows are presented.Comment: Journal of Plasma Physics, 201

Intelligent OFDM telecommunication system. Part 4. Anti-eavesdropping and anti-jamming properties of the system, based on many-parameter and fractional Fourier transforms

In this paper, we aim to investigate the superiority and practicability of many-parameter wavelet and Golay transforms (MPWT and MPGT) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication system (Intelligent-OFDM-TCS), based on many-parameter transforms (MPTs). New system uses inverse MPT for modulation at the transmitter and direct MPT for demodulation at the receiver. The purpose of employing the MPTs is to improve the PHY-LS of wireless transmissions against to the wide-band anti-jamming communication. Each MPT depends on finite set of independent Jacobi parameters (angles), which could be changed independently one of another. When parameters are changed, multi-parametric transform is changed too taking form of a set known (and unknown) orthogonal (or unitary) wavelet transforms. We implement the following performances as bit error rate (BER), symbol error rate (SER), peak to average power ratio (PAPR), the Shannon-Wyner secrecy capacity (SWSC) for novel Intelligent-MPWT-OFDM-TCS. Previous research has shown that the conventional OFDM TCS based on discrete Fourier transform (DFT) has unsatisfactory characteristics in BER, PARP, SWSC and in anti-eavesdropping communications. We study Intelligent-MPT-OFDM-TCS to find out optimal values of angle parameters of MPT optimized BER, PAPR, SWSC, anti-eavesdropping effects. Simulation results show that the proposed Intelligent OFDM-TCS have better performances than the conventional OFDM system based on DFT against eavesdropping. © 2019 IOP Publishing Ltd. All rights reserved