Sharif Digital Repository

Today, we live in an information age where all people can access the vast amount of data in the world by connecting to the Internet.Since the Internet has expanded significantly to share information, some individuals and organizations seek to be able to prevent the possible sabotage of some people by monitoring network users. Analysis of computer network traffic is one of the importance issues that many activities have been done in this area. One of the most important questions in traffic analysis is to identify the main content of traffic on the encrypted network. Numerous studies have shown that the traffic of websites visited through the Tor network, including Specific information that... 

In this work, we aim to find a way to identify and forecast transients in nuclear power plants with the aid of semi-supervised machine learning algorithm. Forecasting and identifying transients in nuclear power plants at the early stages of formation are essential for safety considerations and precautionary measures. The use of machine learning algorithms provides an intelligent control mechanism that, along with the main operator of the power plant, raises the transient detection and identification rate. Our algorithm of choice is to change the way data is presented, which is a semi-supervised learning approach. The algorithm consists of two methods: quantum dynamics clustering... 

Meaningful gesture recognition, whose purpose is to interpret human movements, plays a crucial role in various fields such as human and computer interaction, sign language recognition, robot control, and medical applications. Sign language recognition is regarded as the most significant use of gesture recognition by many researchers. Sign languages are the natural medium of communication for millions of deaf people all over the world, and the existence of a sign language recognition system has significantly aided in facilitating communication between deaf individuals and others. Despite numerous studies conducted in this field in recent years, there are still many challenges to continuous... 

The ultimate goal of this study is to evaluate the condition of concrete structures using computer vision methods and using powerful tools such as machine learning methods based on visual information. This assessment is performed by detecting damage in these structures. With the aging of structures such as dams, bridges and tall buildings, structural health monitoring is an important task in ensuring their safety and stability. Therefore, rapid assessment of the health of structures and diagnosis of damage after destructive events is of great value in terms of providing resilience of structures. Visual inspection of structures by experts is one of the basic methods of evaluating structures.... 

Increasing the popularity of smartphones and the great ovation of users of mobile apps has turned the app stores to massive software repositories. Therefore, using these repositories can be useful for improving the quality of the program. Since the bridge between users and developers of mobile apps is the comments that users write in the app store, special attention to these comments from developers can make a dramatic improvement in final quality of mobile apps. Hence, in recent years, numerous studies have been conducted around the topic of opinion mining, whose intention was to extract and exert important information from user’s reviews. One of the shortcomings of these studies is the... 

Recent success of Deep Learning models, resulted in their evergrowing application in many fields. However these models usually require huge datasets, which can sometimes be hard to collect. One of the challenges related to medical data, is the Batch Effect; Medical data is usually gathered through multiple experiments. Each experiment might have a slightly different conditions than the other, resulting a shift in the data related to that batch. Batch effects can have more severe impact during testing time, as the shift in the data distribution could be bigger. Many methods have been proposed to reduce or remove the effect of external conditions on data distribution.Deep Learning models have... 

In this thesis, we review some methods related to model-free discrete reinforcement learning and their corresponding algorithms. Our main goal is to present existing methods in an integrated and formal setup, without compromising their mathematical accuracy or comprehensibility. We have done our best to fix the inconsistencies existing in notations and definitions appearing in different areas of the vast literature. We discuss dynamic programming methods, including policy iteration and value iteration and temporal difference methods as well as policy-based methods such as policy gradient, advantage actor-critic, TRPO, and PPO. Among value-based methods, we discuss Q-learning and C51 where we... 

Nowadays, video on demand causes a drastic increase in network traffic that it is expected that network traffic surpasses 45 exabytes per month until 2022; consequently, utilizing distributed memories known as caches across the network to alleviate the communication load during peak hours is inevitable. Coded caching is a promising approach to mitigate and smooth traffic during peak hours in the communication network in a way that it creates coded multicasting opportunities in addition to delivering content to users locally. However, it suffers from imposed delay resulting from coding that makes this approach infeasible for delay-sensitive contents, namely video streaming applications. So... 

Entanglement is one of the key resources of quantum information science which makes identification of entangled states essential to a wide range of quantum technologies and phenomena.This problem is however both computationally and experimentally challenging.Here we use autoencoder neural networks to find semi-optimal measurements for detection of entangled states. We show that it is possible to find high-performance entanglement detectors with as few as three measurements. Also, with the complete information of the state, we develop a neural network that can identify all two-qubits entangled states almost perfectly.This result paves the way for automatic development of efficient... 

Gamma ray spectroscopy has a special place in the industrial applications of nuclear radiation. Currently, the most common device for gamma ray detection and spectroscopy is the sodium iodide scintillation detector. Long life and high efficiency and reasonable price of these detectors are the reasons for the development of the use of these detectors in industries and laboratories. But these detectors in the classification of energy sensitive detectors are considered as detectors with low resolution. The presence of broad peaks in the gamma ray spectrum of this detector increases the possibility of interference of peaks related to different energies and makes it difficult to identify the... 

For several years, self-organizing networks have been introduced by 3GPP to reduce the costs of mobile operators. Self-organizing networks, by introducing a kind of intelligence to the mobile network, will cause optimal performance and, as a result, reduce the operating costs of operators. Improving network coverage and increasing user data rates are always among the priorities of operators to increase profits and customer satisfaction. Coverage and capacity optimization is one of the most important functions introduced in self-organized networks. In this study, in addition to the coverage and capacity optimization function, the load balance optimization and the contradictory interaction of... 

Recent advances in deep neural networks have shown significant potential; however, they still face challenges when it comes to non-stationary environments. Continual learning is related to deep neural networks with limited capacity that should perform well on a sequence of tasks. On the other hand, studies have shown that neural networks are sensitive to covariate shifts. But in many cases, the distribution of data varies with time. Domain Adaptation tries to improve the performance of a model on an unlabeled target domain by using the knowledge of other related labeled data coming from a different distribution. Many studies on domain adaptation have optimistic assumptions that are not... 

Cryptocurrencies are not only regarded as a trustworthy method of financial transaction validated by a decentralized cryptographic system as opposed to a centralized authority, but also as one of the most popular and lucrative forms of trade and investing. Predicting the price of a cryptocurrency is a challenging topic in time-series research. Its intricacy is due to the volatility and large swings of cryptocurrencies' price. The emergence of brand-new cryptocurrencies, which might present a profitable trading opportunity but lack sufficient historical data for technical analysis, prompted us to develop a trading strategy that could be applied universally. The forecast of the next timestep's... 

Software requirements are changing continuously and hence during software evolution and maintenance, source codes changes are being committed in the software repositories. Reading source codes to understand the changes is a very time consuming and tedious activity. Commit messages contain information about code changes that let developers be aware of the essence of the changes without reading the source codes. Unfortunately, due to the pressure of deadlines and lack of time, developers neglect to write these messages. Commit messages can speed up the process of software understanding for developers and also play an important role in software documentation. Therefore, an automated method for... 

Ability to differentiate induced pluripotent stem cells to cardiomycocytes has attracted attentions,considering crucial role of the heart in the human body and great potential applications of these cells like disease modeling, new treatment methods and basic research. We are able to analyze the performance of beating cells through recording extracellular field potentials of cardiomyocytes using multi-electrode array (MEA) technology. This analysis is an essential step to use cardiac cells in any future development and experiment. Currently, the electrophysiology experts analyze recorded extracellular field potentials of induced cardiomyocytes by observing all the episodes of each record.... 

In recent years, studying of biological tissues by mass spectrometry imaging (MSI) has been considered due to its selectivity in identifying different compounds in biological tissues, no need for sample preparation, and the possibility of creating the distribution map of these compounds. The complexity of biological tissues due to their heterogeneity, the large volume of data generated, and the effects of competition of other species for ionization in MSI experiments have doubled the importance of using chemometrics to interpret these data. The aim of this work is to quantitatively study Chlordcone as a carcinogenic pesticide and to extract its spatial distribution pattern in mouse liver... 

Ever increasing demand for higher data rate in wireless communication in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio for dynamic access to spectrum. In dynamic spectrum access a new type of users called secondary users measure the spectrum to see if it is occupied by licensed users (primary users or PU). When channel is empty secondary users can use it to transmit signal. This approach is called spectrum sensing. Hidden PU problem can severely defect detection ability of non-cooperativ spectrum sensing systems. Cooperative spectrum sensing (CSS) uses spatial diversity of spectrum sensors to tackle this problem. There are two kinds... 

In the recent years, increasing concerns about the environmental issues necessitatecientists and industrial people to introduce clean and zero emissions vehicles to the global market. Relying on this acute demand,Hybrid Electric Vehicles (HEVs) are knownas one potential solution to lessen the negative effects of transportation industry on ecological systems.HEVs combine an Internal Combustion Engine (ICE) with an Electric Motor/Generator (EMG) and an Energy Storage System (ESS) to produce a fuel efficient and low or zero pollutant powertrain. Increasing the number of components in the powertrain requires a sophisticated strategy to control all drive-line components suitably. Surely, the best... 

The use of IoT-based technologies is expanding in many areas today. The use of audio and video processing in IoT systems has been used as an alternative to human operators by increasing power and reducing processing costs. Due to the large volume of audio and video data and bandwidth limitations, complete data transfer to cloud processing servers is not cost-effective in terms of efficiency and energy consumption. As a result, the solution that has provided good results is to discharge these device tasks to the available clouds. In other words, the capacity of resources in the environment can be used to optimize the total latency of the system and energy consumption. In this dissertation, we... 

In this project an optimized and robust orientation estimation method using IMU and magnetic sensors is presented. Magnetic distortion effects in orientation estimation is also one of the main purposes. Proposed sensor fusion algorithm is based on a complementary filter which provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. To develop the basic sensor fusion algorithm some procedures including a simple calculation to deal better with non-gravitational accelerations, decrease the effect of magnetometer in the presence of distortions and online gyroscope bias estimation is added. Also, a method for classification the different types of...