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White space regions
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 22 January 2011 through 28 January 2011, Novy Smokovec ; Volume 6543 LNCS , 2011 , Pages 226-237 ; 03029743 (ISSN) ; 9783642183805 (ISBN) ; Fazli, M ; Ghodsi, M ; Safari, M ; Saghafian, M ; Tavakkoli, M ; Sharif University of Technology
2011
Abstract
We study a classical problem in communication and wireless networks called Finding White Space Regions. In this problem, we are given a set of antennas (points) some of which are noisy (black) and the rest are working fine (white). The goal is to find a set of convex hulls with maximum total area that cover all white points and exclude all black points. In other words, these convex hulls make it safe for white antennas to communicate with each other without any interference with black antennas. We study the problem on three different settings (based on overlapping between different convex hulls) and find hardness results and good approximation algorithms
Convex Hull Problem in the Uncertain Models
, M.Sc. Thesis Sharif University of Technology ; Ghodsi, Mohammad (Supervisor)
Abstract
Many geometric problems have algorithms that exist on paper for them, such as the Convex Hull problem [1], Minimum Spanning Tree [2], Voronoi Diagram [3], Closest Pair of Points [4], Largest Empty Circle [5], Smallest Enclosing Circle [6], and more. In all of these problems, the assumption is that a certain number of points are given as input, and we must perform various operations on these points based on the nature of the problem. Furthermore, a stronger assumption is that these points are given to us precisely, but in reality, this is not the case, and for various reasons, it is not possible to determine the exact locations of these points. Therefore, it can be said that we are dealing...
Unsupervised Pattern Recognition in DATA Strams
, M.Sc. Thesis Sharif University of Technology ; Zarei, Alireza (Supervisor)
Abstract
Pattern recognition in data streams using bounded memory and bounded time is a difficult task. There are many techniques for recognizing patterns but when we talk about data streams these algorithms became useless since there are no enough memory to store all data. In data stream model the entire data is not available at any time and we don’t have enough time processing each data.
In this thesis we consider current methods for recognizing patterns from a data streams. The goal pattern in this study was the minimum total area of k convex polygons encloses all data
In this thesis we consider current methods for recognizing patterns from a data streams. The goal pattern in this study was the minimum total area of k convex polygons encloses all data
Cooperative spectrum sensing by algorithmic techniques using spatial information
, Article ; 2010 5th International ICST Conference on Communications and Networking in China, ChinaCom 2010, 25 August 2010 through 27 August 2010 , 2010 ; 9780984589333 (ISBN) ; Hemmatyar, M. A ; Fazli, M. A ; Izadi, M ; Sharif University of Technology
Abstract
Spectrum sensing is the basic step in cognitive radio networks. Its accuracy directly affects spectrum utilization. In this paper we propose a new method for blind cooperative spectrum sensing using spatial information. We model our sensing matrix as a graph, taking advantage of signal and spatial correlation of antennas. We prove that the general settings of the problem are hard to approximate. Taking spatial information into account, we propose a new scheme to find white space subregions. Cognitive radio users residing in the region may be able to transmit, but must keep their transmission power to the extent that no interference is caused to primary receivers out of the region