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Modeling of Two Phase Flow in Horizontal Well Coupling Natural Fracture Reservoir
, M.Sc. Thesis Sharif University of Technology ; Shad, Saeed (Supervisor)
Abstract
Today due to increasing demand of using energy and petroleum products, finding optimum ways to produce more oil and gas in a way which petroleum reservoirs faces less pressure drop is very necessary. Therefore, petroleum engineers face multiphase flow proportionally more than other issues. So, they have to comprehend this issue in a complete manner in order to discover the best practical ways for maximum utilization. Researchers in different ways tried to model multiphase flow, but many of provided models are so complicated and sometimes have low accuracy. In this project we tried to present a model that in simplicity has high accuracy and works in a way that apperceiving it, is very...
Towards Spurious Correlation Robustness of Out-of-Distribution Detection Methods
, M.Sc. Thesis Sharif University of Technology ; Rohban, Mohammad Hossein (Supervisor)
Abstract
Many machine learning models make confident decisions when encountering out-of-distribution (OOD) data which differ from their training data distribution. However, these models should not make predictions on unfamiliar samples they have not seen before, and rejecting such unknown samples is crucial for deploying trustworthy models in real-world applications. Consequently, OOD detection has garnered significant attention over the past decade. Despite the development of highly accurate methods to address this issue, there has been little focus on their robustness against various factors. One common factor threatening the robustness of these methods is the presence of spurious correlations in...
Flight envelope expansion in landing phase using classic, intelligent and adaptive controllers [electronic resource]
, Article Journal of aircraft ; 2006, Vol. 43, No. 1 ; Izadi, Hojjat Allah ; Pakme, Mehrdad
Abstract
An expanding flight envelope in the landing phase of a typical jet transport aircraft in presence of strong wind shears using a learning capable control system (LCCS) is investigated. The idea stems fromhuman beings functional architecture that gives them the ability to do more as they age and gain more experience. With the knowledge that classical controllers lack sufficient generality to cope with nonlinear as well as uncertain phenomenon such as turbulent air, the focuse is on different types of intelligent controllers due to their learning and nonlinear generalization capabilities as candidates for the landing flight phase. It is shown that the latter class of controllers could be used...
Identity-based universal re-encryption for mixnets
, Article Security and Communication Networks ; Volume 8, Issue 17 , February , 2015 , Pages 2992-3001 ; 19390114 (ISSN) ; Mohajeri, J ; Salmasizadeh, M ; Sharif University of Technology
John Wiley and Sons Inc
2015
Abstract
In order to provide anonymity, universal cryptosystems have been used in various applications, including mixnets with multiple receivers. Unlike ordinary re-encryption cryptosystems, universal cryptosystems for re-encryption of ciphertexts do not require knowledge of the public key of the receiver. Golle et al. introduced universal cryptosystems for public key cryptography. Contrary to public key cryptography, in ID-based cryptography, a public key infrastructure is not needed, which makes it suitable for situations where it is not cost-effective to distribute certificates or establish a public key infrastructure. In this paper, we first generalize the definition of universal cryptosystems...
All-optical wavelength-routed architecture for a power-efficient network on chip
, Article IEEE Transactions on Computers ; Vol. 63, issue. 3 , 2014 , p. 777-792 ; Hessabi, S ; Sharif University of Technology
2014
Abstract
In this paper, we propose a new architecture for nanophotonic Networks on Chip (NoC), named 2D-HERT, which consists of optical data and control planes. The proposed data plane is built upon a new topology and all-optical switches that passively route optical data streams based on their wavelengths. Utilizing wavelength routing method, the proposed deterministic routing algorithm, and Wavelength Division Multiplexing (WDM) technique, the proposed data plane eliminates the need for optical resource reservation at the intermediate nodes. For resolving end-point contention, we propose an all-optical request-grant arbitration architecture which reduces optical losses compared to the alternative...
Hierarchical opto-electrical on-chip network for future multiprocessor architectures
, Article Journal of Systems Architecture ; Volume 57, Issue 1 , 2011 , Pages 4-23 ; 13837621 (ISSN) ; Hessabi, S ; Sharif University of Technology
2011
Abstract
Importance of power dissipation in NoCs, along with power reduction capability of on-chip optical interconnects, offers optical network-on-chip as a new technology solution for on-chip interconnects. In this paper, we extract analytical models for data transmission delay, power consumption, and energy dissipation of optical and traditional NoCs. Utilizing extracted models, we compare optical NoC with electrical one and calculate lower bound limit on the optical link length below which optical on-chip network loses its efficiency. Based on this constraint, we propose a novel hierarchical on-chip network architecture, named as H2NoC, which benefits from optical transmissions in large scale...
Scalable architecture for a contention-free optical network on-chip
, Article Journal of Parallel and Distributed Computing ; Volume 72, Issue 11 , 2012 , Pages 1493-1506 ; 07437315 (ISSN) ; Hessabi, S ; Sharif University of Technology
2012
Abstract
This paper proposes CoNoC (Contention-free optical NoC) as a new architecture for on-chip routing of optical packets. CoNoC is built upon all-optical switches (AOSs) which passively route optical data streams based on their wavelengths. The key idea of the proposed architecture is the utilization of per-receiver wavelength in the data network to prevent optical contention at the intermediate nodes. Routing optical packets according to their wavelength eliminates the need for resource reservation at the intermediate nodes and the corresponding latency, power, and area overheads. Since passive architecture of the AOS confines the optical contention to the end-points, we propose an electrical...
Power efficient nanophotonic on-chip network for future large scale multiprocessor architectures
, Article Proceedings of the 2011 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2011, 8 June 2011 through 9 June 2011, San Diego, CA ; 2011 , Pages 114-121 ; 9781457709944 (ISBN) ; Hessabi, S ; Sharif University of Technology
2011
Abstract
This paper proposes new architectures for data and control planes in a nanophotonic networks-on-chip (NoC) with the key advantages of scalability to large scale networks, constant node degree, and simplicity. Moreover, we propose a minimal deterministic routing algorithm for the data network which leads to small and simple photonic switches. Built upon the proposed novel topology, we present a scalable all-optical NoC, referred to as 2D-HERT, which offers passive routing of optical data streams based on their wavelengths. Utilizing wavelength routing method, Wavelength Division Multiplexing (WDM) technique, and a new all-optical control architecture, our proposed optical NoC eliminates the...
Hierarchical on-chip routing of optical packets in large scale MPSoCs
, Article Proceedings of the 18th Euromicro Conference on Parallel, Distributed and Network-Based Processing, PDP 2010, 17 February 2010 through 19 February 2010, Pisa ; 2010 , Pages 515-524 ; 9780769539393 (ISBN) ; Hessabi, S ; Sharif University of Technology
2010
Abstract
In this paper, we extract analytical models for data transmission delay, power consumption, and energy dissipation of optical and traditional NoCs. Utilizing extracted models, we compare optical NoC with electrical one for varying values of link length and degree of multiplexing and calculate lower bound limit on the optical link length below which optical on-chip network loses its efficiency. Based on this constraint, we propose a novel hierarchical on-chip network architecture, named as H2NoC, which benefits from optical transmissions in large scale SoCs and overcomes the scalability problem resulted from lower bound limit on the optical link length. Performing a series of simulation-based...
All-Optical Scalable Multi-stage Interconnection Network for Data Centers
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayeh (Supervisor)
Abstract
According to the increasing amount of data exchanged among data centers, the need for speeding up and bandwidth and reduced power consumption has been increased. The information show that about 77% of the data is moved into the data centers. On the other hand, 10% of data center’s power consumption is used to data transmission. Improving the interconnection network of data centers can play an important role in reducing power consumption and speeding up. In recent years, optical interconnects have gained attention as a promising solution. Nevertheless, offering an all-optical and efficient architecture is an important issue. In this study, we intend to provide a multi-stage, all-optical...
Prediction of DNA/RNA Sequence Binding Site to Protein with the Ability to Implement on GPU
, M.Sc. Thesis Sharif University of Technology ; Koohi, Sommaye (Supervisor)
Abstract
Based on the importance of DNA/RNA binding proteins in different cellular processes, finding binding sites of them play crucial role in many applications, like designing drug/vaccine, designing protein, and cancer control. Many studies target this issue and try to improve the prediction accuracy with three strategies: complex neural-network structures, various types of inputs, and ML methods to extract input features. But due to the growing volume of sequences, these methods face serious processing challenges. So, this paper presents KDeep, based on CNN-LSTM and the primary form of DNA/RNA sequences as input. As the key feature improving the prediction accuracy, we propose a new encoding...
Energy Aware Routing Algorithm with SDN in Data Center Networks
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
It is well known that data centres consume high amounts of energy, which has become a major concern in the field of cloud computing. Therefore, energy consumption could be reduced by using intelligent mechanisms work to adapt the set of network components to the total traffic volume. SDN is an efficient way to do so because it has many benefits over traditional approaches, such as centralised management, low capex, flexibility, scalability and virtualisation of network functions. In our work will we use the heuristic energy-aware routing (HEAR) model, which is composed of the proposed heuristic algorithm and the energy-aware routing algorithm. This work identifies the unused links and...
Adopting Dynamic Topology for Energy Management in Optical Interconnection Networks in Data Centers
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayeh (Supervisor)
Abstract
Today with the deployment of cloud computing and web applications; We need to have powerful datacenters with provisioning high bandwidth. Current data centers with electronic network interconnects, using excessive power to provisioning requisite bandwidth. Nevertheless, interconnecting networks in data centers isn’t in maximum efficiency and many components of them aren't used efficiently. So it is necessary to use an optical network with dynamic provisioning variable bandwidth and energy management. In this approach, our proposed architecture is designing topology with adopting dynamically for energy management in optical interconnect networks in data centers. To achieve this we can study...
DNA Classification Using Optical Processing based on Alignment-free Methods
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
In this research, an optical processing method for DNA classification is presented in order to overcome the problems in the previous methods. With improving in the operational capacity of the sequencing process, which has increased the number of genomes, comparing sequences with a complete database of genomes is a serious challenge to computational methods. Most current classification programs suffer from either slow classification speeds, large memory requirements, or both. To achieve high speed and accuracy at the same time, we suggest using optical processing methods. The performance of electronic processing-based computing, especially in the case of large data processing, is usually...
Drug-target Interaction Prediction through Learning Methods for SARS-COV2 Based on Sequence and Structural Data
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
Predicting the binding affinity of drug molecules and proteins is one of the most important stages of drug discovery, development and screening, for which numerous laboratory, simulation and computational solutions have been provided. Laboratory and simulation methods require molecular structures, are time and financial expense, and computational methods do not provide accurate predictions. Therefore, the use of deep neural networks in extracting features from data with a simpler and more accessible structure of protein sequences and molecules solves these challenges with lower cost and higher accuracy. In this article, the use of a new molecular sequence named selfies, which has solved the...
An Efficient Solution for Drug Target Interaction and Binding Affinity Prediction Using Deep Learning Methods
, Ph.D. Dissertation Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
Predicting the interaction and binding affinity of drug-target represents a crucial yet complex phase in the time-consuming and costly process of drug discovery and development. Advances in deep learning have significantly enhanced the ability to model and extract intricate relationships and patterns from diverse biological and pharmaceutical data. However, existing methodologies encounter several fundamental challenges, including the modeling of protein and drug representations, understanding molecular interactions, and overcoming data access limitations. Addressing these challenges has necessitated the use of various heterogeneous architectures, methods, and data structures. Despite these...
Improvement on Deep Learning based Methods of Protein Structure Prediction
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayye (Supervisor)
Abstract
Proteins are natural polymers composed of amino acids, which, according to their structure, can interact with other molecules and play a wide range of roles. Predicting the tertiary structure of proteins is fundamental for explaining their function and for applications such as drug design. Considering the time-consuming and expensive laboratory methods on one hand and the rapid growth and large volume of protein data on the other hand, a faster solution for finding protein structure in large scales is needed. With the increasing progress of artificial intelligence and neural networks, the use of this approach to predict the three-dimensional structure of proteins using their amino acid...
MHC-Peptide Binding Prediction Using a Deep Learning Method with Efficient GPU Implementation Approach
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on feature extraction from the peptide and MHC sequences separately and ignore their valuable binding information. This paper develops a capsule neural network-based method to efficiently capture and model the peptide-MHC complex features to predict the peptide- MHC class I binding. Various evaluations over multiple datasets using popular performance metrics...
Motif Finding Application Using Edit Distance Approuch
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
Motif finding problem in biology is a search for repeated patterns to reveal information about gene expression, one of the most complex subsystems in genomics. ChIP-seq technology abled researchers to investigate location of protein-DNA interactions but analyzing downstream results of such experiments to find actual regulatory signals in genome is challenging. For many years, applications of motif finding had models based on limiting assumption as an exchange for lower computational complexity. Results: AKAGI program is build upon upgraded methods and new general models to investigate statistical and experimental evidences for accurately finding significant patterns among biological...
Free space Optical Spiking Neural Network
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
Due to the increasing volume of data in various fields, existing electronic processors face a major challenge. While processing power has increased, solving complex problems in a timely manner remains a major challenge for today's processors. Neuromorphic engineering offers a potential solution by looking to processors found in nature, such as the human brain. This field of research involves investigating natural processors and designing new ones based on these models. To address issues related to manufacturing and integrating transistors, increasing processor costs, and the limitations of Moore's law, it is possible to use analog signals, such as sound or light, instead of electrical...