Sharif Digital Repository

In recent years, the application of machine learning algorithms for predicting physical phenomena and achieving a balance between problem-solving speed and accuracy has gained prominence. The propagation of radio waves in indoor environments, due to various phenom- ena such as multipath signals, requires a significant amount of time for simulation. This thesis explores the phenomenon of indoor wave propagation using machine learning algorithms. By utilizing physical features, the error in the predicted received power map is reduced. These features include the presence of a line-of-sight ray, the number of obstacles between the transmitter and receiver, the distance between the transmitter... 

Integrity of closed-loop systems with integral action control is characterized by using a series of successively more difficult to obtain conditions: Integral Stabilizable (IS), Integral Controllable (IC), Integral Controllable with Integrity (ICI), and Decentralized Integral Controllability (DIC). The IS condition requires that for a Linear Time-Invariant (LTI) finitedimensional (FD) plant, G(s), one finds a stabilizing decentralized controller with integral action. For G(s) to be IC, in addition to being IS, one should be able to reduce the gain of
all control loops by the same factor from a finite value to (but not including) zero without introducing instabilities. ICI requires that... 

Considering the supply and demand market of natural gas, methanol, propylene and ethylene and the propylene value chain, it is expected that the design of the propylene production process from methanol produced from natural gas and its implementation in Iran country can significantly flourish the production of polypropylene, acrylonitrile and Etc. On the other hand, with the industrialization of this process, the uncontrolled export of methanol from Iran to countries such as China and the devaluation of methanol will be prevented. In this report, the process of producing synthetic gas from natural gas using autothermal reactor and heat exchange reforming, separation and storage of carbon... 

Effective field theory is a practical method for model building and explaining experimental/observational data. These theories are constructed based on knowing low energy degrees of freedom and symmetries (accidental or fundamental). By construction, these theories are applicable up to a cut-off scale and their predictions beyond that are not reliable. For applications to higher energies a UV completion is needed through which modifications are applied or new heavy degrees of freedom are introduced. In order to complete a theory in a Wilsonian approach, scattering amplitudes must satisfy some constraints. These constraints are derived from basic assumptions like Lorentz invariance,... 

In this thesis, we first explain how we can construct three-point amplitudes for both massive and massless particles by solely knowing the Lorentz invariance and spin or helicity of particles. Then, using BCFW recursion relation, we determine the tree-level amplitudes. To calculate the loop amplitude, we apply the unitarity of Smatrix.The unitarity relates the tree-level amplitudes to loop level. Finally, I discuss some exciting topics in this field, including color-kinematics duality, some relevant applications of contemporary S-matrix theory in effective field theories  

Nowadays capacitive deionization (CDI) has attracted a lot of attention for water treatment. CDI is an emerging water treatment technology that uses electrophoretic driving forces for desalination of water. During the CDI process, ions are adsorbed onto the surface of electrodes by applying a low voltage electric field (DC<2V). In addition, the regeneration of the electrodes contains desorption of the electrosorbed ions from the surface of the electrodes to the water in the absence of the applied electric field. In the mechanism of CDI, separation and accumulation of ions in the electric field are the main processes and no additional chemicals are required in this technology. Therein,... 

Information cascade or affection in a broad social network is introduced as a dynamic epidemic phenomenon in the society. As people notify a new innovation, technology, or hobby, they try to share it with their friends, colleges or neighbors. Till now most of the cascade models are presented for unsigned network, in which all links have the same sign (such as friends and trusted networks). In these networks cascade is independent of the edge sign. But in reality signed networks are as common as simple networks. Thus, in this thesis, we study information cascade in networks with positive and negative edges. We link the cascade size to community structure of signed networks; communities are... 

By generalizing dictionary learning (DL) algorithms to Multidimensional (MD) mode and using them in applications where signals are inherently multidimensional, such as in three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging, it is possible to achieve much higher speed and less computational complexity. In this thesis, the formulation of the Multidimensional Dictionary Learning (MDDL) problem is expressed and six algorithms are proposed to solve it. The first one is based on the method of optimum directions (MOD) algorithm for 1D dictionary learning (1DDL), which uses alternating minimization and gradient projection approach. As the MDDL problem is non-convex, the second... 

Membership inference attacks are one of the most important privacy-violating attacks in machine learning, as well as infrastructure of more serious attacks such as data extraction attacks. Since membership inference attack is used as a measure to evaluate the level of privacy protection of machine learning models, different researches have investigated and provided new methods for this attack. However, the accuracy of these attacks has not been investigated on models trained with the latest techniques such as data augmentation and regularization techniques. In this research, we see that the Lira attack, the latest membership inference attack, which has much more power compared to previous... 

Mankind with it’s finite resources (Time, Energy, …) cannot make use of every accessible option in daily activities (such as buying items, listening to music and reading news), and is restricted to decide on a handful of them. Available options are increasing on a daily basis and these surplus of available options had an adverse effect; Thus, leading us to more baffling situations. As a result, need for external assistance appeared in decision situations. Considering exceptional computation power available to computers, a framework named Recommender Systems were developed. Recommender systems try to use their accessible data in order to make fitting suggestions to users. Personalization and... 

As the amount of data on the Internet grows, users face diverse options while searching for their desired information and items. Therefore, accessing what one is looking for, is usually time consuming and even impossible in some cases. In order to solve this issue, the goal of recommender systems is to offer recommendations which are compatible with users’ needs and preferences. One of the most important challenges of recommender systems is to improve the quality of recommendations. Recommender systems’ quality can be assessed using different metrics including precision, novelty and coverage. However, these metrics are inconsistent in some applications and improving one will cause a decline... 

Hypertension precvalence is 24 and 20.5 percent in men and women, respectively. Continuous Blood Pressure monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This work presents an efficient algorithm, based on the Pulse Arrival Time (PAT), extracted from Electrocardiogram (ECG) and Photopletysmograph (PPG), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. Methods: The proposed framework estimates the BP values through processing vital... 

Continuous Blood Pressure monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This work presents an efficient algorithm, based on the Pulse Arrival Time (PAT), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the... 

The rapid growth of computer networks has increased the importance of analytics and traffic analysis tools for these networks, and the increasing importance of these networks has increased the importance of security of these networks and the intrusion detection in these networks. Many studies aimed at providing a powerful way to quickly and accurately detect computer network intrusions, each of which has addressed this issue.The common point of all these methods is their reliance on the features extracted from network traffic by an expert. This strong dependence has prevented these methods from being flexible against new attacks and methods of intrusion or changes in the current normal... 

Hi-C is a new technology invented to record the amount of interaction between all chromatin fragments. Chromatin interactions are manifestations of chromatin's 3D structure. It has been proved that different 3D structures of different cell types, despite having the same DNA, causes in different functionalities of cells. Therefore knowing chromatin structure, the factors affecting it and the way it affects cells' behaviors such as gene co-expression, sheds more light on the knowledge about cells and the methods that can change their fates. Finding the causes of diseases, designing more efficient drugs and progressing the conversion of different cell types into each other used for amending... 

With the rise of the concept of “Big Data”, both data volumes and data processing time increased, imposing the need for new methods of processing and computation of said data.Analytical and computational methods in Machine Learning are some of the most important applications of Big Data processing. There exist many methods of data analysis in the Machine Learning field, each requiring extensive processing on Big Data. One of the methods for working with Big Data is Distributed Systems. MapReduce is one of the most popular methods distributed computation by increasing the ease and speed of distributed processing of big data. But a number of bottlenecks have been discovered in MapReduce which... 

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 recent years, highly recommended systems have been used in various areas. One of the approaches of these systems is a collaborative refinement that consists of three user-based, item-based, and matrix-based parsing. Matrix degradation methods are more effective because they allow us to discover the hidden features that exist between user and item interactions and help us better predict recommendations. The low-level mapping method is designed to store and process very high volume of data. In this method, after completing computations in the author’s nodes, the data is sent to the downsizing nodes, which is referred to as ”data spoofing”. It has been observed that in many applications, the... 

Autism spectrum disorder (ASD) is a strong genetic perturbation that encompasses a wide range of clinical symptoms, including functional at different regions of the brain, repetitive behaviors, and interests, weaknesses in social relationships, some sensitivities to environmental factors and etc. Genetic complexity and the impact of environmental factors put the disease in the category of Level 1 complex developmental disorders.We proposed a pilot, combined, and highly effective structure to identify biomarkers in the autism spectrum disorder that could be extended to other diseases that have a similar genetic architecture with autism. We also develop a Gene-tissue interaction network to... 

Internet of things (IOT) interconnects a system of low power embedded devices that each of them has a unique identifier, without human intervention. The number of devices of IOT increases every day, therefor routing in the network of IOT is an important challenge. So, Internet Engineering Task Force (IETF) propose a protocol for routing low power and lossy networks in 2012, which is named RPL. Considering the power supply limitation of devices in the network, utilizing energy-aware policies within the RPL is so important. Meanwhile, objective functions have a vital role in the consuming energy. Also, energy consumption determines the lifetime of nodes. Since many of devices are placed in...