Information and System Group

February 2013

No Date Title Speaker Presentation Discussion
1 2013-02-07 Performance Analysis of Iterative Decoding Algorithms with Memory over Memoryless Channels Jeongmin     (PDF)
In this work, they propose a model for iterative decoding algorithms with memory which covers successive relaxation (SR) version of belief propagation and differential decoding with binary message passing (DD-BMP) algorithms. Based on this model, they derive a Bayesian network for iterative algorithms with memory over memoryless channels and use this representation to analyze the performance of the algorithms using density evolution.
2 2013-02-14  Faster STORM using compressed sensing Eunseok  (PDF)
 In super-resolution microscopy methods based on single-molecule switching, the rate of accumulating single-molecule activation events often limits the time resolution. Here we developed a sparse-signal recovery technique using compressed sensing to analyze images with highly overlapping fluorescent spots. This method allows an activated fluorophore density an order of magnitude higher than what conventional single-molecule fitting methods can handle. Using this method, we demonstrated imaging microtubule dynamics in living cells with a time resolution of 3 s.
3 2013-02-21 A Node-Based Time Slot Assignment Algorithm for STDMA Wireless Mesh Networks Muhammad Asif Raza (PDF)
In this paper authors present a link capacity model for spatial time-division multiple access (STDMA) mesh networks. It makes use of a simplified transmission model that also considers channel fading. The model then forms the basis of a node-based slot-assignment and scheduling algorithm. This algorithm enables the user to exploit multiuser diversity that results in optimizes network throughput. The presented algorithm shows significant improvement in the throughput when compared with existing slot-assignment methods.
4 2013-02-28  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration Hwanchol Jang  (pdf)
In this paper, the authors introduces TwIST algorithms, exhibiting much faster convergence rate than IST for ill-conditioned problems. For a vast class of nonquadratic convex regularizers, they show that TwIST converges to a minimizer of the objective function, for a given range of values of its parameters.