Développement d'une nouvelle technique de compression pour les codes variables à fixes quasi-instantanés

Pas toutes les techniques de compression des données adoptent le principe de dictionnaire pour représenter ses mots de code. Le dictionnaire est un ensemble de mots de code associés aux symboles sources lors de l’opération d’encodage. La correspondance entre le mot de code et le symbole source dépend de l’algorithme de compression adopté. Généralement, chaque algorithme construit son dictionnaire selon un ensemble de propriétés. Parmi ces propriétés nous avons celle de préfixe. Elle est primordiale pour les codes de type fixe à variable (FV) tels que l’algorithme de Huffman et celui de Shannon-Fano. Par contre, la propriété préfixe est optionnelle pour les codes de longueur variable à fixe (VF). Donc cela peut causer le but de pouvoir construire un dictionnaire plus performant, comme le cas des codes quasi-instantanés. Dans cette optique, Yamamoto et Yokoo ont éliminé cette condition pour créer un dictionnaire meilleur que celui de Tunstall. Les dictionnaires proposés par Yamamoto et Yokoo sont appelés les codes VF quasi-instantanés ou en anglais almost instantaneous VF codes. En s’appuyant sur leurs contributions, nous avons déduit que leur technique peut fournir dans certains cas des codes variables à fixes sous-optimaux, d’où notre suggestion de correctifs à leurs algorithmes pour en améliorer l’efficacité. Aussi nous proposons un autre mécanisme pour construire des codes VF en utilisant le principe de la programmation dynamique.Various techniques of data compression use a dictionary to represent their codewords. A dictionary is a set of codewords associated with the source symbols during the encoding operation. The correspondence between the codeword and the symbol source depends on the compression algorithm. Usually, the prefix property is key for the fixed-to-variable type codes FV as demonstrated in the Huffman and the Shannon-Fano algorithms. However, such a property may be eliminated for fixed-length codes in order to build a more efficient dictionary. In this context, Yamamoto and Yokoo excluded this condition to create a dictionary better than Tunstall. This new dictionary is called instantaneous variable-to-fixed code. Based on their contributions, we have deduced that their technique can provide, in some cases, suboptimal variable-to-fixed codes. Hence, we suggested to improve their algorithms. Also, we proposed another mechanism for building optimal AIVF codes by adopting the principle of dynamic programming

Multiple Beamforming with Perfect Coding

Perfect Space-Time Block Codes (PSTBCs) achieve full diversity, full rate, nonvanishing constant minimum determinant, uniform average transmitted energy per antenna, and good shaping. However, the high decoding complexity is a critical issue for practice. When the Channel State Information (CSI) is available at both the transmitter and the receiver, Singular Value Decomposition (SVD) is commonly applied for a Multiple-Input Multiple-Output (MIMO) system to enhance the throughput or the performance. In this paper, two novel techniques, Perfect Coded Multiple Beamforming (PCMB) and Bit-Interleaved Coded Multiple Beamforming with Perfect Coding (BICMB-PC), are proposed, employing both PSTBCs and SVD with and without channel coding, respectively. With CSI at the transmitter (CSIT), the decoding complexity of PCMB is substantially reduced compared to a MIMO system employing PSTBC, providing a new prospect of CSIT. Especially, because of the special property of the generation matrices, PCMB provides much lower decoding complexity than the state-of-the-art SVD-based uncoded technique in dimensions 2 and 4. Similarly, the decoding complexity of BICMB-PC is much lower than the state-of-the-art SVD-based coded technique in these two dimensions, and the complexity gain is greater than the uncoded case. Moreover, these aforementioned complexity reductions are achieved with only negligible or modest loss in performance.Comment: accepted to journa

Assessment of RANS turbulence models and Zwart cavitation model empirical coefficients for the simulation of unsteady cloud cavitation

The numerical simulation of unsteady cavitation flows is sensitive to the selected models and associated parameters. Consequently, three Reynolds Average Navier-Stokes (RANS) turbulence models and the Zwart cavitation model were selected to assess their performance for the simulation of cloud cavitation on 2D hydrofoils. The experimental cavitation tests from a NACA65012 hydrofoil at different hydrodynamic conditions were used as a reference to tune the modeling parameters and the experimental tests from a NACA0015 were finally used to validate them. The effects of near wall grid refinement, time step, iterations and mesh elements were also investigated. The results indicate that the Shear Stress Transport (SST) model is sensitive to near wall grid resolution which should be fine enough. Moreover, the cavitation morphology and dynamic behavior are sensitive to the selection of the Zwart empirical vaporization, Fv, and condensation, Fc, coefficients. Therefore, a multiple linear regression approach with the single objective of predicting the shedding frequency was carried out that permitted to find the range of coefficient values giving the most accurate results. In addition, it was observed that they provided a better prediction of the vapor volume fraction and of the instantaneous pressure pulse generated by the main cloud cavity collapse.Postprint (published version

Performance bounds for expander-based compressed sensing in Poisson noise

This paper provides performance bounds for compressed sensing in the presence of Poisson noise using expander graphs. The Poisson noise model is appropriate for a variety of applications, including low-light imaging and digital streaming, where the signal-independent and/or bounded noise models used in the compressed sensing literature are no longer applicable. In this paper, we develop a novel sensing paradigm based on expander graphs and propose a MAP algorithm for recovering sparse or compressible signals from Poisson observations. The geometry of the expander graphs and the positivity of the corresponding sensing matrices play a crucial role in establishing the bounds on the signal reconstruction error of the proposed algorithm. We support our results with experimental demonstrations of reconstructing average packet arrival rates and instantaneous packet counts at a router in a communication network, where the arrivals of packets in each flow follow a Poisson process.Comment: revised version; accepted to IEEE Transactions on Signal Processin