Fluid dynamics in porous media with Sailfish

In this work we show the application of Sailfish to the study of fluid dynamics in porous media. Sailfish is an open-source software based on the lattice-Boltzmann method. This application of computational fluid dynamics is of particular interest to the oil and gas industry and the subject could be a starting point for an undergraduate or graduate student in physics or engineering. We built artificial samples of porous media with different porosities and used Sailfish to simulate the fluid flow through in order to calculate permeability and tortuosity. We also present a simple way to obtain the specific superficial area of porous media using Python libraries. To contextualize these concepts, we test the Kozeny--Carman equation, discuss its validity and calculate the Kozeny's constant for our artificial samples.Comment: 13 pages, 12 figure

Symmetry-preserving discretization of variational field theories

The present paper develops a variational theory of discrete fields defined on abstract cellular complexes. The discrete formulation is derived solely from a variational principle associated to a discrete Lagrangian density on a discrete bundle, and developed up to the notion of symmetries and conservation laws for solutions of the discrete field equations. The notion of variational integrator for a Cauchy problem associated to this variational principle is also studied. The theory is then connected with the classical (smooth) formulation of variational field theories, describing a functorial method to derive a discrete Lagrangian density from a smooth Lagrangian density on a Riemannian fibered manifold, so that all symmetries of the Lagrangian turn into symmetries of the corresponding discrete Lagrangian. Elements of the discrete and smooth theories are compared and all sources of error between them are identified. Finally the whole theory is illustrated with the discretization of the classical variational formulation of the kinematics of a Cosserat rod

Joint Iterative Power Allocation and Linear Interference Suppression Algorithms in Cooperative DS-CDMA Networks

This work presents joint iterative power allocation and interference suppression algorithms for spread spectrum networks which employ multiple hops and the amplify-and-forward cooperation strategy for both the uplink and the downlink. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression. We derive constrained linear minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problems, we develop cost-effective algorithms for adaptive joint power allocation, and estimation of the parameters of the receiver and the channels. An analysis of the optimization problem is carried out and shows that the problem can have its convexity enforced by an appropriate choice of the power constraint parameter, which allows the algorithms to avoid problems with local minima. A study of the complexity and the requirements for feedback channels of the proposed algorithms is also included for completeness. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing non-cooperative and cooperative schemes.Comment: 9 figures; IET Communications, 201

Joint Power Adjustment and Interference Mitigation Techniques for Cooperative Spread Spectrum Systems

This paper presents joint power allocation and interference mitigation techniques for the downlink of spread spectrum systems which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to an individual power constraint and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problem efficiently, we develop joint adaptive power allocation and interference suppression algorithms that can be implemented in a distributed fashion. The proposed stochastic gradient (SG) and recursive least squares (RLS) algorithms mitigate the interference by adjusting the power levels across the relays and estimating the parameters of the linear receiver. SG and RLS channel estimation algorithms are also derived to determine the coefficients of the channels across the base station, the relays and the destination terminal. The results of simulations show that the proposed techniques obtain significant gains in performance and capacity over non-cooperative systems and cooperative schemes with equal power allocation.Comment: 6 figures. arXiv admin note: text overlap with arXiv:1301.009

Low-Rank Signal Processing: Design, Algorithms for Dimensionality Reduction and Applications

We present a tutorial on reduced-rank signal processing, design methods and algorithms for dimensionality reduction, and cover a number of important applications. A general framework based on linear algebra and linear estimation is employed to introduce the reader to the fundamentals of reduced-rank signal processing and to describe how dimensionality reduction is performed on an observed discrete-time signal. A unified treatment of dimensionality reduction algorithms is presented with the aid of least squares optimization techniques, in which several techniques for designing the transformation matrix that performs dimensionality reduction are reviewed. Among the dimensionality reduction techniques are those based on the eigen-decomposition of the observed data vector covariance matrix, Krylov subspace methods, joint and iterative optimization (JIO) algorithms and JIO with simplified structures and switching (JIOS) techniques. A number of applications are then considered using a unified treatment, which includes wireless communications, sensor and array signal processing, and speech, audio, image and video processing. This tutorial concludes with a discussion of future research directions and emerging topics.Comment: 23 pages, 6 figure

Study of Sparsity-Aware Distributed Conjugate Gradient Algorithms for Sensor Networks

This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We present sparsity-aware conventional and modified distributed CG algorithms using $l_{1}$ and log-sum penalty functions. The proposed sparsity-aware diffusion distributed CG algorithms have an improved performance in terms of mean square deviation (MSD) and convergence as compared with the consensus least-mean square (Diffusion-LMS) algorithm, the diffusion CG algorithms and a close performance to the diffusion distributed recursive least squares (Consensus-RLS) algorithm. Numerical results show that the proposed algorithms are reliable and can be applied in several scenarios.Comment: 1 figure, 7 page

6-cycle double covers of cubic graphs

A cycle double cover (CDC) of an undirected graph is a collection of the graph's cycles such that every edge of the graph belongs to exactly two cycles. We describe a constructive method for generating all the cubic graphs that have a 6-CDC (a CDC in which every cycle has length 6). As an application of the method, we prove that all such graphs have a Hamiltonian cycle. A sense of direction is an edge labeling on graphs that follows a globally consistent scheme and is known to considerably reduce the complexity of several distributed problems. In [9], a particular instance of sense of direction, called a chordal sense of direction (CSD), is studied and the class of k-regular graphs that admit a CSD with exactly k labels (a minimal CSD) is analyzed. We now show that nearly all the cubic graphs in this class have a 6-CDC, the only exception being K4.Comment: This version fixes typos and minor technical problems, and updates reference

Set-Membership Conjugate Gradient Constrained Adaptive Filtering Algorithm for Beamforming

We introduce a new linearly constrained minimum variance (LCMV) beamformer that combines the set-membership (SM) technique with the conjugate gradient (CG) method, and develop a low-complexity adaptive filtering algorithm for beamforming. The proposed algorithm utilizes a CG-based vector and a variable forgetting factor to perform the data-selective updates that are controlled by a time-varying bound related to the parameters. For the update, the CG-based vector is calculated iteratively (one iteration per update) to obtain the filter parameters and to avoid the matrix inversion. The resulting iterations construct a space of feasible solutions that satisfy the constraints of the LCMV optimization problem. The proposed algorithm reduces the computational complexity significantly and shows an enhanced convergence and tracking performance over existing algorithms.Comment: 3 figure

Robust Adaptive Beamforming Based on Low-Complexity Shrinkage-Based Mismatch Estimation

In this work, we propose a low-complexity robust adaptive beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. LOCSME only requires prior knowledge of the angular sector in which the actual steering vector is located and the antenna array geometry. LOCSME does not require a costly optimization algorithm and does not need to know extra information from the interferers, which avoids direction finding for all interferers. Simulations show that LOCSME outperforms previously reported RAB algorithms and has a performance very close to the optimum.Comment: 5 pages, 2 figures. IEEE Signal Processing Letters, 201

Adaptive Delay-Tolerant Distributed Space-Time Coding Based on Adjustable Code Matrices for Cooperative MIMO Relaying Systems

An adaptive delay-tolerant distributed space-time coding (DSTC) scheme that exploits feedback is proposed for two-hop cooperative MIMO networks. Maximum likelihood (ML) receivers and adjustable code matrices are considered subject to a power constraint with a decode-and-forward (DF) cooperation strategy. In the proposed delay-tolerant DSTC scheme, an adjustable code matrix is employed to transform the space-time coded matrix at the relay nodes. Least-squares (LS) algorithms are developed with reduced computational complexity to adjust the parameters of the codes. Simulation results show that the proposed algorithms obtain significant performance gains and address the delay issue for cooperative MIMO systems as compared to existing delay-tolerant DSTC schemes.Comment: 4 figure