Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition

A new representation of spatio-temporal random processes is proposed in this work. In practical applications, such processes are used to model velocity fields, temperature distributions, response of vibrating systems, to name a few. Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations, for instance, in a computational mechanics problem. For a single-parameter process such as spatial or temporal process, the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Loeve (KL) decomposition. However, for multiparameter processes such as a spatio-temporal process, the covariance function itself can be defined in multiple ways. Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants. Then the spatial covariance matrix at different time instants are considered to define the covariance of the process. This set of square, symmetric, positive semi-definite matrices is then represented as a third-order tensor. A suitable decomposition of this tensor can identify the dominant components of the process, and these components are then used to define a closed-form representation of the process. The procedure is analogous to the KL decomposition for a single-parameter process, however, the decompositions and interpretations vary significantly. The tensor decompositions are successfully applied on (i) a heat conduction problem, (ii) a vibration problem, and (iii) a covariance function taken from the literature that was fitted to model a measured wind velocity data. It is observed that the proposed representation provides an efficient approximation to some processes. Furthermore, a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL, both in terms of computer memory and execution time.

Distributed Coding for Multiple Access Communication with Side Information

In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant, common decoder over a discrete time multiple access channel under various side information assumptions. In particular, we derive an achievable rate region for this communication problem.

Lossy distribution source coding with side information

In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant and common decoder. In particular, we derive inner and outer bounds on the rate region for the random field to be estimated with a given mean distortion.

Temporal Moments for Reactive Transport through Fractured Impermeable/Permeable Formations

The transport of reactive solutes through fractured porous formations has been analyzed. The transport through the porous block is represented by a general multiprocess nonequilibrium equation (MPNE), which, for the fracture, is represented by an advection-dispersion equation with linear equilibrium sorption and first-order transformation. An implicit finite-difference technique has been used to solve the two coupled equations. The transport characteristics have been analyzed in terms of zeroth, first, and second temporal moments of the solute in the fracture. The solute behavior for fractured impermeable and fractured permeable formations are first compared and the effects of various fracture and matrix transport parameters are analyzed. Subsequently, the transport through a fractured permeable formation is analyzed to ascertain the effect of equilibrium sorption, rate-limited sorption, and the multiprocess nonequilibrium transport process. It was found that the temporal moments were nearly identical for the fractured impermeable and permeable formations when both the diffusion coefficient and the first-order transformation coefficient were relatively large. The multiprocess nonequilibrium model resulted in a smaller mass recovery in the fracture and higher dispersion than the equilibrium and rate-limited sorption models. DOI: 10.1061/(ASCE)HE.19435584.0000586. (C) 2012 American Society of Civil Engineers.

VIRTUAL VIEW SYNTHESIS BY NON-LOCAL MEANS FILTERING USING TEMPORAL DATA

A novel algorithm for Virtual View Synthesis based on Non-Local Means Filtering is presented in this paper. Apart from using the video frames from the nearby cameras and the corresponding per-pixel depth map, this algorithm also makes use of the previously synthesized frame. Simple and efficient, the algorithm can synthesize video at any given virtual viewpoint at a faster rate. In the process, the quality of the synthesized frame is not compromised. Experimental results prove the above mentioned claim. The subjective and objective quality of the synthesized frames are comparable to the existing algorithms.