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Moran Vatelmacher, 
M.Sc. student under the supervision of Prof. Shai Avidan

Monday, June 22, 2015 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering

Coherent Clustering and its Use for Image Denoising

Abstract

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). Today, the classical clustering methods are based on appearance information only. This information is sometimes insufficient, especially in the case of noisy data where there is a need to differentiate inliers from outliers. The human eye can sometimes see that a certain area belongs to a certain cluster even if the appearance distance from another cluster is shorter. This is partially done using context analysis. When using context information it is easier to identify outliers. This kind of analysis can help in image analysis applications such as image denoising - discussed here - for which segmentation of the image into clusters is relevant. Our results show that better clustering improves denoising.

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Speaker: Adi Guzi Yitzhak
M.Sc. student under the supervision of Prof. David Burshtein

Wednesday, June 10th, 2015 at 15:30
Room 011, Kitot Bldg., Faculty of Engineering
Polar Codes for Noisy Write Once Memories
Abstract

Write once memories (WOM) are storage devices in which it is impossible or costly
to erase some memory cells. Flash memory is a one important example.
The problem of rewriting on a noisy WOM is considered.
We present a Blahut-Arimoto algorithm for computing the max sum rate of the noisy WOM, and show some numerical results.
A coding scheme for the noisy WOM using polar codes is also presented.
It is shown empirically that the scheme is close to the max sum-rate of noisy
WOMs when an arbitrary number of multiple writes is permitted.
The encoding and decoding complexities scale as O(N logN) where N is the blocklength.

~~Speaker: Anatoly Khina
Ph.D. student under the supervision of Prof. Uri Erez

Wednesday, July 8th, 2015 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering

Simultaneous Matrix Decompositions for Gaussian Communication Networks

Abstract
A central concept in matrix analysis is the decomposition of a matrix into a product of orthogonal (or unitary) matrices and a diagonal/triangular one, e.g., unitary diagonalization of a symmetric matrix, and more generally the singular-value decomposition, and the QR decomposition. Such decompositions are of particular importance for multi-antenna point-to-point physical-layer communications, where the channel gains are represented by a (channel) matrix. Transforming the channel matrix into diagonal/triangular forms, in this case, allows to reduce the coding task to that of coding for scalar (single-antenna) channels. Thus, the modulation and coding tasks are effectively decoupled and the performance is dictated by the diagonal values. In this work we develop new joint matrix decompositions of several matrices using the same unitary matrix on one side (corresponding to a joint transmitter or receiver) to achieve desired properties for the resulting diagonals. An important special case is a transformation leading to equal diagonals for all matrices simultaneously. This, in turn, allows to construct practical schemes for various communications settings, as well as deriving new theoretic bounds for others.