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Helical antenna to measure radiated power density around a BTS: Design and implementation
, Article Proceedings of 3rd Asia-Pacific Conference on Antennas and Propagation, APCAP 2014 ; 18 December , 2014 , Pages 185-188 ; Rassaei, F ; Sharif University of Technology
2014
Abstract
A helical antenna is a helical shaped conductor winded around a cylinder. This antenna can radiate in different modes but the axial mode is one of the most commonly used ones since in this mode, it gives the maximum radiation power. This paper presents a novel, simple and inexpensive method to measure the radiated power density around base transceiver stations (BTS). We designed an optimized helical antenna for this application. Our simulation results show that it is possible to measure the power density with about 10% error, which is acceptable for this application. Furthermore, we implemented the designed antenna based on our analytical results and calibrate it. Experimental tests have...
Hierarchical Fat-tree Topology for an Optical Network-on-Chip
, M.Sc. Thesis Sharif University of Technology ; Hessabi, Shahin (Supervisor)
Abstract
With increasing number of processors on a chip, the role of interconnections becomes more important in both power consumption and bandwidth. As a result, in MultiProcessor System-on-Chip architectures, the design constraints will shift from "Computational Constraints" to "Communicational Constraints". Nowadays, optical information transfer is introduced as a suitable substitution for electrical interconnections in chips, which can eliminate their problems. Many different optical networks have been presented so far. These networks can be divided into two subcategories. Networks of the first category use an electrical infrastructure as well as optical one. Hence, the scalability of scheme is...
Accelerated dictionary learning for sparse signal representation
, Article 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, 21 February 2017 through 23 February 2017 ; Volume 10169 LNCS , 2017 , Pages 531-541 ; 03029743 (ISSN); 9783319535463 (ISBN) ; Sadeghi, M ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Springer Verlag
2017
Abstract
Learning sparsifying dictionaries from a set of training signals has been shown to have much better performance than pre-designed dictionaries in many signal processing tasks, including image enhancement. To this aim, numerous practical dictionary learning (DL) algorithms have been proposed over the last decade. This paper introduces an accelerated DL algorithm based on iterative proximal methods. The new algorithm efficiently utilizes the iterative nature of DL process, and uses accelerated schemes for updating dictionary and coefficient matrix. Our numerical experiments on dictionary recovery show that, compared with some well-known DL algorithms, our proposed one has a better convergence...
Oxidative Deprotection of Trimethylsilyl Ethers and Coupling of Thiols to Disulfides by Cr (VI) and Fe (III) Heterogeneous Nanocatalysts
, M.Sc. Thesis Sharif University of Technology ; Saeedi, Mohammad Reza (Supervisor) ; Rajabi, Fateme (Supervisor)
Abstract
Considering the advantages of heterogeneous catalysts for the easy work up procedure, being eco-friendly, and the reusability, herein, two projects are carried out by metal-supported SBA-15 catalysts. The first one is the oxidative deprotection of trimethylsilyl ethers catalyzed Cr (VI) in the presence of TBHP as oxidant. In this project, trimethylsylil ethers are oxidized in good to high yields. The second project is the oxidative coupling of thiols to disulfides catalyzed by Fe (III) in the presence of hydrogenperoxide. This catalyst/oxidant system convets different aromatic thiols to corresponding disulfides efficiently
Non-Uniform MRI Scan Time Reduction Using Iterative Methods
, M.Sc. Thesis Sharif University of Technology ; Marvasti, Farrokh (Supervisor) ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Magnetic Resonance Imaging is one of the most advanced medical imaging procedure that noninvasively played in most applications. However, this imaging method is a good resolution, but not in the conventional high speed imaging method, in fact, the main problem is slow. In recent years many studies have been done to accelerate MRI that compressed sensing can be mentioned among them. Such methods, however, have had very good results but MRI systems are very complex. This project investigates the reconstruction of MR images using data from partial non-Cartesian samples aimed at reducing sampling time and also speed up the process of reconstruction of MR images have been studied. In this regard,...
Feasibility Study of Using the Elemental Analysis of Prompt Gamma Spectrum to Improve the Treatment Planning in Hadron Therapy
, Ph.D. Dissertation Sharif University of Technology ; Vosoughi, Naser (Supervisor) ; Riazi, Zafar (Supervisor) ; Rasouli, Fateme (Co-Supervisor)
Abstract
Hadron therapy is one of the cancer treatment methods using the targeted dose distributhion. In hadron therapy, the prompt gamma is produced from excited nucleas of target in the following of non-elastic nuclear interactions between the target and the incident proton within few nano-seconds and with energy less than 10 MeV. The excited energy level depends on incident particle energy and the target materials. Since the Spatial distribution of prompt gamma rays depends on incident particles energy, it can be useful for determinding the incident particle range. Also, the prompt gamma energy spectrum of each element is an individual feature, thereby targets with different composition of...
Sparse signal recovery using iterative proximal projection
, Article IEEE Transactions on Signal Processing ; Volume 66, Issue 4 , 2018 , Pages 879-894 ; 1053587X (ISSN) ; Sadeghi, M ; Babaie Zadeh, M ; Chatterjee, S ; Skoglund, M ; Jutten, C ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify...