Directed Transmission Method, A Fully Asynchronous approach to Solve Sparse Linear Systems in Parallel

In this paper, we propose a new distributed algorithm, called Directed Transmission Method (DTM). DTM is a fully asynchronous and continuous-time iterative algorithm to solve SPD sparse linear system. As an architecture-aware algorithm, DTM could be freely running on all kinds of heterogeneous parallel computer. We proved that DTM is convergent by making use of the final-value theorem of Laplacian Transformation. Numerical experiments show that DTM is stable and efficient.Comment: v1: poster presented in SPAA'08; v2: full paper; v3: rename EVS to GNBT; v4: reuse EVS. More info, see my web page at http://weifei00.googlepages.co

Virtual Transmission Method, A New Distributed Algorithm to Solve Sparse Linear System

In this paper, we propose a new parallel algorithm which could work naturally on the parallel computer with arbitrary number of processors. This algorithm is named Virtual Transmission Method (VTM). Its physical backgroud is the lossless transmission line and microwave network. The basic idea of VTM is to insert lossless transmission lines into the sparse linear system to achieve distributed computing. VTM is proved to be convergent to solve SPD linear system. Preconditioning method and performance model are presented. Numerical experiments show that VTM is efficient, accurate and stable. Accompanied with VTM, we bring in a new technique to partition the symmetric linear system, which is named Generalized Node & Branch Tearing (GNBT). It is based on Kirchhoff's Current Law from circuit theory. We proved that GNBT is feasible to partition any SPD linear system.Comment: v1: short paper to describe VTM, published by NCM'08; v2: add an example of level-two splitting; v3: full paper; v4: rename EVS to GNBT; add lines coupling technique; v5: reuse EVS, get rid of GNBT; more info, see http://weifei00.googlepages.co

Adaptive low rank and sparse decomposition of video using compressive sensing

We address the problem of reconstructing and analyzing surveillance videos using compressive sensing. We develop a new method that performs video reconstruction by low rank and sparse decomposition adaptively. Background subtraction becomes part of the reconstruction. In our method, a background model is used in which the background is learned adaptively as the compressive measurements are processed. The adaptive method has low latency, and is more robust than previous methods. We will present experimental results to demonstrate the advantages of the proposed method.Comment: Accepted ICIP 201