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fatemi-dokht--mahdieh
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Total 238 records
Homogeneous Einstein Spaces with Negative Scalar
, M.Sc. Thesis Sharif University of Technology ; Fannaei, Hammid Reza (Supervisor)
Abstract
All known examples of homogeneous Einstein spaces with negative scalar curvature (non compact) are isometric to standard Einstein solvmanifolds . we prove that any nilpotent Lie algebra having a codimension-one abelian ideal is the nilradical of a rank –one Einstein solvmanifold . In other words this nilpotent Lie algebra admits a rank-one solvable extension which can be endowed with an Einstein left invariant Riemannian metric . also a curve of pairwise non-isometric 8-dimensional rank-one Einstein solvmanifold is given .
Design and fabrication of an ubiquitous, low-cost, and wearable respiratory bio-sensor using ionic soft materials
, Article 26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, 27 November 2019 through 28 November 2019 ; 2019 , Pages 55-59 ; 9781728156637 (ISBN) ; Mirzaei, I ; Esmaeili Dokht, P ; Fardmanesh, M
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
The respiratory system is a vital organ system which makes breathing and gas exchange possible for human's body. The loss of functionality in this system is due to either genetical problems or environmental issues like pollution or toxic gases which in many cases is inevitable. These circumstances may cause different respiratory diseases which may not be curable presently but their side effects can be partly controlled by careful and timely analysis improving the quality of patient's life. So, an appropriate device for conducting different analysis can be effective for people who suffer from respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, occupational lung...
Test case prioritization using test case diversification and fault-proneness estimations
, Article Automated Software Engineering ; Volume 29, Issue 2 , 2022 ; 09288910 (ISSN) ; Mirian Hosseinabadi, S. H ; Mahdieh, M ; Sharif University of Technology
Springer
2022
Abstract
Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use auxiliary sources of data such as bug history can be interesting. We aim to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we...
Design and Fabrication of Impedance Flowcytometric based Microfluidic Sorter
, M.Sc. Thesis Sharif University of Technology ; Fardmanesh, Mehdi (Supervisor)
Abstract
Microfluidic technology is a field of science that deals with the design, fabrication, and experimental tests of small-scale fluid systems and has made great strides over the past decade. As an interdisciplinary field, this growing field of technology has many applications in medicine, diagnostics, chemical analysis, electronics industry, etc. One of the primary applications of the microfluidic devices is the development of " laboratory on-chip (LOC)" tools as point-of-care (POC) diagnostic tools, such as, rapid detection during surgery. A LOC device includes various application modules, sample transfer and preparation modules, separation modules, and detection and analysis modules. The...
A novel, low cost, and accessible method for rapid fabrication of the modifiable microfluidic devices
, Article Scientific Reports ; Volume 10, Issue 1 , 2020 ; Esmaeili Dokht, P ; Fardmanesh, M ; Sharif University of Technology
Nature Research
2020
Abstract
As microfluidic chips are evolving to become a significant analysis tool toward POCT devices, it is crucial to make the cost and the time required for the fabrication process of these chips as low as possible. Because of the multidisciplinary nature of these systems and the collaboration of many different laboratories and organizations from vastly various fields with unequal types of equipment, it is essential to develop new techniques and materials to make the integration of disparate systems together more straightforward, accessible, and economical. In this paper, we present ethylene–vinyl acetate (EVA) as a new polymer-based material for the fabrication of different microfluidic chips,...
Multi-Modal Distance Metric Learning
, M.Sc. Thesis Sharif University of Technology ; Soleymani, Mahdieh (Supervisor)
Abstract
In many real-world applications, data contain multiple input channels (e.g., web pages include text, images and etc). In these cases, supervisory information may also be available in the form of distance constraints such as similar and dissimilar pairs from user feedbacks. Distance metric learning in these environments can be used for different goals such as retrieval and recommendation. In this research, we used from dual-wing harmoniums to combining text and image modals to a unified latent space when similar-dissimilar pairs are available. Euclidean distance of data represented in this latent space used as a distance metric. In this thesis, we extend the dual-wing harmoniums for...
Fabrication and Characterization of Thermoplastic Starch Based Nanocomposite for Bone Scaffold
, M.Sc. Thesis Sharif University of Technology ; Bagheri, Reza (Supervisor)
Abstract
This project aimed to fabricate the bone scaffolds with applying thermoplastic starch-based nano-biocomposites. The starting materials for this scaffold are as follows: thermoplastic starch, ethylene vinyl alcohol as the polymer matrix and nanoforsterite as the ceramic reinforcing phase. Furthermore, vitamin E was used as antioxidant for preserving starch against thermo-mechanical degradations. Likewise, 3D pore structure was developed using azo-dicarbonamide and water in injection moulding process. With blending thermoplastic starch and ethylene vinyl alcohol, some thermoplastic starch’s properties including degradation rate and water absorption were modified. In addition, having...
Unsupervised Domain Adaptation via Representation Learning
, M.Sc. Thesis Sharif University of Technology ; Soleymani, Mahdieh (Supervisor)
Abstract
The existing learning methods usually assume that training and test data follow the same distribution, while this is not always true. Thus, in many cases the performance of these learning methods on the test data will be severely degraded. We often have sufficient labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and no labeled training data. In this thesis, we study the problem of unsupervised domain adaptation, where no labeled data in the target domain is available. We propose a framework which finds a new representation for both the source and the target domain in which the distance between these...
Deep Learning for Multimodal Data
, M.Sc. Thesis Sharif University of Technology ; Soleymani, Mahdieh (Supervisor)
Abstract
Recent advances in data recording has lead to different modalities like text, image, audio and video. Images are annotated and audio accompanies video. Because of distinct modality statistical properties, shallow methods have been unsuccessful in finding a shared representation which maintains the most information about different modalities. Recently, deep networks have been used for extracting high-level representations for multimodal data. In previous methods, for each modality, one modality-specific network was learned. Thus, high-level representations for different modalities were extracted. Since these high-level representations have less difference than raw modalities, a shared...
Deep Learning For Recommender Systems
, M.Sc. Thesis Sharif University of Technology ; Soleimani, Mahdieh (Supervisor)
Abstract
Collaborative fltering (CF) is one of the best and widely employed approaches in Recommender systems (RS). This approach tries to fnd some latent features for users and items so it would predict user rates with these features. Early CF methods used matrix factorization to learn users and items latent features. But these methods face cold start as well as sparsity problem. Recent years methods employ side information along with rating matrix to learn users and items latent features. On the other hand, deep learning models show great potential for learning effective representations especially when auxiliary information is sparse. Due to this feature of deep learning, we use deep learning to...
Adversarial Networks for Sequence Generation
, M.Sc. Thesis Sharif University of Technology ; Soleymani, Mahdieh (Supervisor)
Abstract
Lots of essential structures can be modeled as sequences and sequences can be utilized to model the structures like molecules, graphs and music notes. On the other hand, generating meaningful and new sequences is an important and practical problem in different applications. Natural language translation and drug discovery are examples of sequence generation problem. However, there are substantial challenges in sequence generation problem. Discrete spaces of the sequence and challenge of the proper objective function can be pointed out.On the other, the baseline methods suffer from issues like exposure bias between training and test time, and the ill-defined objective function. So, the...
Improving Sampling Efficiency of Probabilistic Graphical Models
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of...
Deep Zero-shot Learning
, M.Sc. Thesis Sharif University of Technology ; Soleymani, Mahdieh (Supervisor)
Abstract
In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. On the other hand, with recent advances made by deep neural networks in computer vision, a rich representation can be obtained from images that discriminates different categorizes and therefore obtaining a unsupervised information from images is made possible. However, in the previous works, little attention has been paid to using such unsupervised information for the task of zero-shot learning. In this...
NAFAS: non-rigid air flow active sensor, a cost-effective, wearable, and ubiquitous respiratory bio-sensor
, Article IEEE Sensors Journal ; Volume 21, Issue 7 , 2021 , Pages 9530-9537 ; 1530437X (ISSN) ; Esmaeili Dokht, P ; Khazaee Nejad, S ; Fardmanesh, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
A Non-rigid Air Flow Active Sensor (NAFAS) to detect the respiratory patterns is introduced in this paper. The main part of NAFAS is an ionic electroactive polymer-based ( ${i}$ -EAP) soft sensor. It can accurately detect breathing patterns and has promising features to develop low cost, wearable, and ubiquitous spirometers. Patients with respiratory diseases need to take pulmonary function tests consistently. Hence, it is time-consuming and expensive. To address these problems, NAFAS presents point-of-care testing (POCT) of the respiratory system's function. The proposed ${i}$ -EAP based sensor is a small strip of Ionic Polymer Metal Composite (IPMC) that has been fabricated using a Nafion...
Stabilization of robots with actuator constraints via interconnection and damping assignment
, Article IEEE Transactions on Control Systems Technology ; Volume 31, Issue 6 , 2023 , Pages 2945-2952 ; 10636536 (ISSN) ; Namvar, M ; Dokht Taghirad, H. R ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2023
Abstract
Actuator limitations hinder high-performance control of robotic manipulators. The problem is particularly challenging in case of underactuated robots where the issue has received less attention. In this brief, we investigate stabilization of manipulators under input constraints via total energy shaping. For this purpose, we use interconnection and damping assignment passivity-based control (IDA-PBC) together with an optimization technique to keep actuator torque close to allowable limits. Using a subsequent technique, the actuator torques are ensured to stay within the specified bounds for special classes of manipulators. The results are verified through simulations on a 2 degree-of-freedom...
The regulated market for kidneys in Iran
, Article Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, 22 August 2011 through 23 August 2011 ; Volume 80 LNICST , August , 2012 , Pages 62-75 ; 18678211 (ISSN) ; 9783642309120 (ISBN) ; Sharif University of Technology
2012
Abstract
We study the kidney market in Iran. The most effective treatment for end-stage renal disease is a kidney transplant. While the supply of cadaveric kidneys is limited, the debate has been focused on the effects of the existence of a free market for human organs. Economists as well as medical and legal researchers are divided over the issue. Iran has a unique kidney market which has been in place for over 20 years, frequently reporting surprising success in reducing the waiting list for kidneys. This paper demonstrates how the Iranian system works
Improving Graph Construction for Semi-supervised Learning in Computer Vision Applications
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Semi-supervised Learning (SSL) is an extremely useful approach in many applications where unlabeled data can be easily obtained. Graph based methods are among the most studied branches in SSL. Since neighborhood graph is a key component in these methods, we focus on methods of graph construction in this project. Graph construction methods based on Euclidean distance have the common problem of creating shortcut edges. Shortcut edges refer to the edges which connect two nearby points that are far apart on the manifold. Specifically, we show both in theory and practice that using geodesic distance for selecting and weighting edges results in more appropriate neighborhood graphs. We propose an...
The effect of geometrical properties of reservoir shale barriers on the performance of Steam-assisted Gravity Drainage (SAGD)
, Article Energy Sources, Part A: Recovery, Utilization and Environmental Effects ; Volume 34, Issue 23 , 2012 , Pages 2178-2191 ; 15567036 (ISSN) ; Sharif University of Technology
2012
Abstract
Many bitumen reservoirs contain shale layers of varying thickness, lateral extent, and frequency. These shale layers, depending on their size, vertical and horizontal locations, and continuity throughout the reservoir, may act as a flow barrier and severely reduce vertical permeability of the pay zone and slow down the steam-assisted gravity drainage steam chamber development. Therefore, to improve productivity in these reservoirs, understanding of the effects of reservoir heterogeneities has become necessary. This work presents numerical investigation of the effects of shale barriers on steam-assisted gravity drainage performance when applied to produce mobile heavy oil. The most concern of...
The applicability of expanding solvent steam-assisted gravity drainage (ES-SAGD) in fractured systems
, Article Petroleum Science and Technology ; Volume 28, Issue 18 , Oct , 2010 , Pages 1906-1918 ; 10916466 (ISSN) ; Sharif University of Technology
2010
Abstract
The aim of this contribution is to evaluate the performance of an expanding solvent steam assisted gravity drainage (ES-SAGD) process in naturally fractured systems. Steam-assisted gravity drainage (SAGD) and ES-SAGD processes have been investigated in both conventional and fractured reservoir models and the effect of networked fractures on the recovery mechanism and performance of ES-SAGD has been investigated. Operational parameters such as steam quality, vertical distances between wells, and steam injection temperature have been also evaluated. Finally, to study the effect of a well's horizontal offset, a staggered ES-SAGD well configuration has been compared to a stacked ES-SAGD
Simulation study of Steam Assisted Gravity Drainage (SAGD) in fractured systems
, Article Oil and Gas Science and Technology ; Volume 64, Issue 4 , 2009 , Pages 477-487 ; 12944475 (ISSN) ; Sharif University of Technology
2009
Abstract
The Steam Assisted Gravity Drainage (SAGD) process, a developed Enhanced Oil Recovery (EOR) process to recover oil and bitumen, has been studied theoretically and experimentally in conventional reservoirs and models and is found a promising EOR method for certain heavy oil reservoirs. In this work simulation studies of the SAGD process were made on different fractured models consisting of fractures in both Near Well Region (NWR) and Above Well Region (AWR) and even in the presence of networked fractures. At early stage of the SAGD process in fractured system, steam moves through the fractures first and then the matrix blocks are heated primarily by conduction and possibly some steam...