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Speaker: Eyal Radiano
M.Sc. student under the supervision of Dr. Ofer Amrani

Wednesday, June 17th, 2015  at  15:30
Room 011, Kitot Bldg., Faculty of Engineering

On Disaster Recovery in OFDMA Environment
Abstract

In this talk we describe a communication approach referred to as Random Orthogonal Frequency Division Multiple Access (R-OFDMA). This approach addresses directly issues of receiver complexity, collision handling, protocol overheads and scalability.
Among the applications of this method reside such that require strategic survivability of the network (emergency services, public safety, and more).
This approach addresses common problems of protocol based communication techniques, such as resource allocation management, collision detection and retransmission. These problems burden a lot of overhead on the system utilization and throughput, especially in situations of increased collisions and retransmissions when the network tries to recover.
In the wireless domain, a key objective of R-OFDMA is to segment between steady state and disaster; it does not aim for steady state daily situations where the currently proposed 4G solution is good enough.
R-OFDMA assumes vast number of end users trying to communicate with data bursts simultaneously, but only prioritized active users that are responsible for recovery of the network will be active simultaneously.
An important ingredient of R-OFDMA is based on spectrum sensing, where errors on its part may cause false alarms and miss identification events, which can potentially degrade the quality-of-service experienced by active users.
In this work, we describe the high level mechanism of ROFDMA, accompanied by problem formulation and we develop an analytical model in order to reflect different event classes caused by spectrum sensing.

~~Noam Zac, 
M.Sc. student under the supervision of Prof. Nahum Kiryati and Prof. Nir Sochen

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

Restoring an image of a moving object from a turbulence distorted video

Abstract

Restoring a clean image of a static scene from a turbulent video, using de-warping and de-blurring, was suggested in previous works. In this thesis a novel approach for restoring a clean image of a moving object from a turbulent video is introduced. The previously suggested scheme is generalized by using weighting maps in order to focus each block of the algorithm on the object of interest and disregard the background. The dense optic flow method suggested by Brox et al (2004) was generalized by integrating the weighting maps into the functional in order to focus on certain areas. In addition, the functional suggested by Bar et al (2006) is generalized to use the weighting maps to focus the performance of the de-blur algorithm on areas of interest. Both simulated and real turbulent videos are used to evaluate and show the benefits of using the weighting maps at different blocks of the algorithm.