Sharif Digital Repository

Recently, there has been much interest in sparse channel estimation, i.e. recovering a channel which has much less non zero tabs than its length. These channels have been observed in underwater and broadband wireless channels. In the last few years methods available to estimate these channels have used sparse structure information to improve the estimates. However, these methods are vulnerable to noise and interference. In other words, these methods do not use channel posterior information obtained from the received signal and this is detrimental to the estimator performance. In order to solve these problems in this thesis, motivated by CoSAMP algorithm which is a sparse signal processing... 

As you know in lean supply chains, it is tried to: reduce the wastes of resources and achieve more production with lower consumption of resources. This system is very well suited for many supply chains in which the final determinant factors are price and quality. In contrast, recently, the concept of agile supply chain has been introduced, insisting on flexibility and high service level. Opposite to lean supply chain, agile supply chain needs safety stock to be able to respond to market’s changes.
Regarding the dissimilarities of these models –including the disagreement in the allocation of safety stock to nodes– In this thesis, in addition to studying and comparing these two approaches,... 

Existing face forgery detection methods mainly focus on certain features of images, such as features related to image noise, local textures or frequency statistics of images for forgery detection. This makes the extracted representations and the final decision depend on the data in the database and makes it difficult to detect forgery with unknown manipulation methods. Solving this challenge, which is called the generalization challenge in artificial intelligence literature, has become the main goal of researchers in this field. In this thesis, the focus is on extracting effective features for success in forgery detection and preventing the performance of the forgery detection network from... 

One of the most important problems in machine learning is investigating the performance of the learning algorithms, and especially deep neural networks, on adversarial examples, which are generated by imperceptibly perturbing input images, so that cause the model make a wrong prediction. Not only this line of research is important for making deep neural networks dependable, but also can help with understanding the fundamental limitations of deep neural networks, and the nature of their operation, which can in turn provide researchers with valuable insights into artificial intelligence. In this research work, we have tried to approach the topic with a mainly theoretical mindset. The method we... 

Wind energy technologies guarantee low pollution and operational costs. Using a DFIG and a fractionally rated power electronics converter gives variable speed operation with a low cost drive train. As energy policy organizations have allocated a considerable quota of wind energy generation to offshore wind farms, the absence of slip rings and brushes in the brushless DFIG (BDFIG) is an advantage for offshore wind turbines where maintenance is vital and expensive. With increasing wind power penetration in power systems, grid code requirements are an important consideration for the ride-through capability of wind farms through voltage dips, particularly for multi- MW wind turbine generators.... 

In this project a knee prostheses wear testing simulator based on a parallel mechanism for applying forces and torques, is designed. First according to ISO 14243 standard required degrees of freedom and design constraints are determined and also regarding simulator requirements some considerations in design are outlined. Considering all of these parameters, literature has been searched for proper parallel mechanism and because no suitable parallel mechanism, which is a 3 DoF 2T1R paralle mechanism with rotational degree about z axis, has been found; several new designs of proper mechanism have been presented. Scoring different mechanisms according to outcomed parameters from standard and... 

We study the expected value of stopping times in stochastic processes. Since there is no rigorous solution for computing stopping times in many processes, our approach is based on estimation using well-known methods in the Reinforcement Learning literature. The primary method in this research is the temporal difference algorithm. With some modifications, we can study the role of some state features in determining the stopping time. Moreover, without a complicated mathematical analysis, we can find functions closely enough to the goal function.Furthermore, we compare our proposed algorithm to the well-known regression methods and show our algorithm's advantages and disadvantages. The primary...