~~
Speaker: Doron Shinbox
M.Sc. student under the supervision of Dr. Dan Raphaeli

Wednesday, March 16th, 2016 at 15:30
Room 011, Kitot Bldg., Faculty of Engineering

Advanced Iterative Equalization Methods

Abstract
In this work, we address a well studied problem of the iterative equalization (uncoded and coded). Even though that the two known uncoded iterative equalizers, the ISDIC and the MMSE ISDIC equalizers, were introduced and investigated more than a decade ago, there is no practical solution to the high complexity of the MMSE ISDIC and the low performance of the ISDIC. In this work, we introduce a novel, low complexity and high performance equalizer, the Multiple Symbol (MS) ISDIC equalizer. The MS ISDIC equalizer is the first equalizer that bridges the gap between the low complexity ISDIC equalizer to the high complexity MMSE ISDIC. In addition, we introduce the Constant Filter MS ISDIC (CF MS ISDIC), which is even a lower complexity version of MS ISDIC equalizer. Then, the two MS ISDIC versions will be incorporated into the Turbo Equalizer (TEQ) scheme, resulting in an enhanced TEQ, that perform better than the conventional, state of the art, linear TEQ, with lower complexity and lower amount of iterations.

~~(The talk will be given in English)

Speaker:   Prof. Andrei Sharf
                       Computer Science Department, Ben Gurion University

Monday, March 14th, 2016
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering

Recent Results in 3D scanning

Abstract
The evolution of 3D scanners has made it possible to acquire a large variety of objects such as urban scenes, underwater and lately even motion. The initial representation of the scene consists of several properly transformed depth images, resulting in a point sampling of the object's surface.
Typically, 3D scan data consist of missing parts, noise and outliers.One of today's principal challenges is the development of robust point processing and reconstruction techniques that deal with the inherent inconsistencies in the acquired data set.

In my talk I will present recent advances in processing 3D points data. Motivated by recent advancements in sparse signal reconstruction, I will present a "lower-than-L2" minimization  scheme for sparse reconstruction. The sparsity principle gives rise to a  novel global reconstruction paradigm for sharp point set surfaces which is  robust to noise. Next, I will present a supervised learning algorithm for understanding cluttered indoor scenes. We argue that object classification cannot be directly applied to the scanned scene, since object segmentation is unavailable. Moreover, the segmentation problem is as challenging as classification since spatial relationships between points and patches are neither complete nor reliable.  Our key idea is to interleave the segmentation and classification computations, defining a novel search-classify scene understanding framework.

Short bio
ANDREI SHARF is an associate professor at the computer science department at Ben-Gurion University. Previously, he has been a Visiting Associate Professor at the Shenzhen Institute of Advanced Technology(SIAT) Chinese Academy of Sciences and a Postdoctoral researcher at the School of Computer Science in UC-Davis U.S. His research interests are in computer graphics, including geometry processing, interactive techniques, urban modeling and motion reconstruction. In 2012, Sharf received the Eurographics young researcher award for his contributions to the field of 3D point clouds and in a range of related problems.

Single walled carbon nanotubes as optical sensors at the nanoscale

Dr.  Gili Bisker

Department of Chemical Engineering, Massachusetts Institute of Technology

Optimal dosing of rotavirus vaccination in Japan