Sharif Digital Repository

One of the most important goals we pursue in telecommunications science is to send and receive information from telecommunication channels. By designing a powerful telecommunication system consisting of a transmitter and a receiver, we achieve this goal. Speed of data transmission, accuracy of received information and speed of data extraction are some of the criteria by which the performance of a telecommunication system can be evaluated. No telecommunication channel is free of noise. For this reason, additional information is added to the original information in the transmitter, which can still be extracted if the original information is noisy. This process is called coding. Following... 

In the communication systems, the raw signals of information are mainly encoded so as to prevent the detrimental effects of channel noises and distortions. After some processes, this encoded signal is passed through the channel. At the receiver side, the received signal has to be decoded to extract the information signal. In order to decode the received signal, the receiver require prior knowledge about the encoder parameters. The traditional approach is to send the encoder parameters along with the encoded signals. However, this transmission overhead might occupy a considerable amount of bandwidth since the type of coding may alter several times in a fraction of a second based on the... 

The loss function form in deep networks has been the subject of numerous studies in machine learning communities in recent years. Despite the fact that the problem of optimization in these networks has high dimensions and usually non-convex, gradient-based algorithms have acceptable results in practical experiments. In other words, the convergence point is usually a low-cost local minimum, and conventional optimization algorithms do not get stuck in high-cost local minima. It has been shown that this phenomenon occurs if the number of network weights is much greater than the number of training samples, and it is referred to as the over parameterization region. Recently, several studies have... 

Nowadays, due to the rapid growth of electric energy consumption at the distribution network level and the environmental problems caused by conventional generation units, it has become necessary to provide appropriate solutions to increase the penetration of renewable energy sources and optimally manage the consumers/prosumers at the demand side of power systems. In recent years, various methods have been proposed for demand-side energy management models and distributed energy resources (DERs) integration into the distribution network. Amongst them, the transactive coordination approach that employs the economic and control mechanisms for the management of DERs and responsive loads has been... 

In some applications of distributed systems, agents use weak communication models instead of sending and receiving messages to each other. Beeping, Stone-age, and Population protocols are examples of weak communication models. For example, population protocols are currently used in a variety of areas, such as biological systems (like molecular programming).In this model, the agents are initialized based on their own values. These agents then wake up according to their local clocks and interact with other agents randomly. During these interactions and updating states repeatedly, the agents converge to their final states, which is the expected outcome of the problem.We consider the problem of... 

We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing... 

Given a social network modeled as a weighted graph G, the influence maximization problem seeks k vertices to become initially influenced, to maximize the expected number of influenced nodes under a particular diffusion model. The influence maximization problem has been proven to be NP-hard, and most proposed solutions to the problem are approximate greedy algorithms, which can guarantee a tunable approximation ratio for their results with respect to the optimal solution. The state-of-the-art algorithms are based on Reverse Influence Sampling (RIS) technique, which can offer both computational efficiency and non-trivial (1-1/e-ϵ)-approximation ratio guarantee for any ϵ>0. RIS-based... 

One of the key objectives in many fields in machine learning is to discover causal relationships among a set of variables from observational data. In linear non-Gaussian acyclic models (LiNGAM), it can be shown that the true underlying causal structure can be identified uniquely from merely observational data. DirectLiNGAM algorithm is a well-known solution to learn the true causal structure in high dimensional setting. DirectLiNGAM algorithm executes in a sequence of iterations and it performs a set of comparisons between pairs of variables in each iteration. Unfortunately, the runtime of this algorithm grows significantly as the number of variables increases.In this thesis, we propose a... 

Well stimulation techniques are used to increase the productivity of oil and gas wells. One of the effective well stimulation methods, is matrix acidizing. During matrix acidizing, acidic solution is injected into the formation at pressure below the formation fracture pressure. Most of the time, acid needs to be reached to low permeability layers for more efficient damage removal. Divertors are used for diverting acid from high permeability to low permeability layers.Successful performance of a viscoelastic acid during an acid job, is highly dependent on the surfactant rheological properties. For a betaine viscoelastic surfactant, we have studied the effect of different parameters on the... 

Nowadays we are witnessing the emergence of machine learning in various applications. One of the key problems in data science and machine learning is the problem of dimensionality reduction, which deals with finding a mapping that embeds samples to a lower-dimensional space such that, the relationships between the samples and their properties are preserved in the secondary space as much as possible. Obtaining such mapping is essential in today's high-dimensional settings. Moreover, due to the large volume of data and high-dimensional samples, it is infeasible or insecure to process and store all data in a single machine. As a result, we need to process data in a distributed manner.In this...