Sharif Digital Repository

SAR image noise is a significant problem for SAR image analysis.The inherent noise of SAR images, known as speckle, seriously affects the SAR image interpretation. It also has adverse effects on the classification and segmentation of SAR images. Due to its great significance, the SAR image processing has received considerable attention in recent years and many researchers have developed techniques to reduce the inherent noise accompanying the SAR images. A survey of the literature shows that the wavelet analysis is one of the most common methods used for speckle reduction. While the power of the morphological analysis method has mostly not been recognized, we have utilized this efficient... 

Image segmentation is a technique which divides an image into significant parts. The accuracy of this technique plays an important role when it applies on medical images. Among various image segmentation methods, clustering methods have been extensively investigated and used. Since it is an unsupervised method, the existence of a small amount of side-information which is extracted from a specific application (in this case, medical image) could improve its accuracy. Using this side-information in clustering methods introduces a new generation of clustering approaches called semi-supervised clustering. This information usually has a format of pair-wise constraints and can be prepared easily... 

The large number of unit tests produced in the test-driven development (TDD) method and the iterative execution of these tests extend the regression test execution time in TDD. This study aims to reduce test execution time in TDD. We propose a TDD-based approach that creates traceable code elements and connects them to relevant test cases to support regression test selection during the TDD process. Our proposed hybrid technique combines text and syntax program differences to select related test cases using the nature of TDD. We use a change detection algorithm to detect program changes. Our experience is reported with a tool called RichTest, which implements this technique. To evaluate our... 

Iris segmentation is an essential step in a medical imaging-based automated diagnosis system. The operation of dividing a digital image into areas with different characteristics and setting objectives is known as image segmentation. The extraction of the iris from unnecessary sections of the image is very important for any biometric device. Eyelid / eyelash obstruction, special reflections, intensity heterogeneity, and an irregular iris border, are considered as noise which affect the iris segmentation process. Focusing on Fuzzy C-Means (FCM) clustering and Grasshopper Optimization Algorithm (GOA), we present a new and efficient method for dividing iris-filled, annoying, and iris limits that... 

Sensor networks is a set of sensors (nodes) which are communicating with each other, and gather information from different environments, and process them to give us valuable results. Sensors have a small energy source like a small battery which have to be used efficiently. Congestion detection and avoidance, together are of great importance in Wireless Sensor Networks (WSNs) in case of traffic load, data loss, and power consumption. In this work, we propose a new APS layer (application support sub-layer), which is another key standard component of Application layer, offering a well-defined interface and several control services. It functions as a bridge between the network layer and other... 

Safety assessment of the final disposal of radioactive waste is crucial to ensure the waste isolation from the biosphere for long periods of time. There are various scenarios for the radionuclide transport from the disposal site to the environment, among which the ground water scenario is considered as the main scenario of the radionuclides migration. In this scenario, the transport parameters of the radionuclides are very important in the safety assessment of the final disposal of radioactive wastes. These parameters are determined using either dynamic or static methods, which usually the dynamic method leads to more realistic results. In the dynamic method, one of the two approaches of... 

In this thesis, a Segmented Gama Scanning System for measuring total activity of the low/intermediate level radioactive waste drums is designed and a prototype 3-segmented commercial system is manufactured by using NaI detectors. The optimum distance of detector from drum surface and the optimum number of detectors on reconstruction of the radioactivity of the waste drum were studied by MCNP simulations. In addition, the effects of inhomogeneous distribution of radioactivity, the addition of lead filters to detectors, the change in collimator length and the asymmetrical placement of a drum on the rotary plate were also examined by the MCNP calculations. In the absence of a real radioactive... 

In this thesis, a computer program is produced to measure the activity of 137Cs and 60Co radionuclides in waste packages generated at Bushehr Nuclear Power Plant (BNPP). In this program, the activity of waste drum was calculated by dividing the waste matrix into very small identical cubic cells, and each cell was considered as a point source (in the center of the cell). The activity was calculated using the theory developed by Krings & Mauerhofer. Detection efficiencies were calculated by the developed program at different conditions of a) different detector distance from drum surface, b) different collimator length, c) different cell size and d) up to 4 segment number. The results were... 

Today, a lot of efforts have been done in order to commercialize space travels. Spacecrafts have a lot of components and producing each of them spends high costs. Components such as propellant tanks and boosters are multi segment and are released simultaneously and therefore it is economically feasible to recover them. Due to Parafoil ability in low-cost, soft (no damage to the cargo) and accurate landing, it is gradually replacing parachute. Parafoil can fly long distances after being released, so its landing point could be determined. The aim of the study is to evaluate this scenario in which some segments of launch vehicle (e.g. booster) that are equipped with Parafoil expand their... 

Among businesses operating under the sharing economy model, ride-sharing platforms or Internet taxis have attracted a lot of attention and have been able to encourage millions of people to use them in a few years. Meanwhile, what has been the subject of numerous researches in recent years is the motivation of people to use this type of business. Understanding what motivates users to use these businesses is important to them because it helps them allocate their resources optimally by focusing on the benefits, shortcomings, and potential disadvantages of their product, And then by improving the provided service, have more advantages and attractiveness in the eyes of customers. Accordingly, in... 

In recent years, the tourism industry has become one of the most influential industries in the income generation of countries and has attracted the attention of researchers. Identifying the important features of the hotel from the users' point of view is one of the areas that have been considered, while the segmentation of hotel customers based on the extracted features has been less in the focus of researchers and the need for more research in this field has been felt by experts.Given that the desire to return to the hotel by travelers has always been one of the factors affecting the financial performance of hotels, the factors affecting it are of great importance. It can be very valuable... 

Mobile and wireless services industry has been among the most attractive businesses recently and is progressing at a fast pace. In this huge industry various organizations and entities cooperate to deliver value to the end user. The mobile operator plays the pivotal role in this chain. Hence mobile operator’s market was chosen in this study as the representative of the industry. Market segmentation and customer satisfaction are the main topics of this study.
Market segmentation for MCI was done using data mining with two-step algorithm. Input data included the consumer behavior extracted from mobile post-paid bills. The results indicated five distinctive clusters. The next part included... 

Banks and financial services typically focus on geographic, demographic, socio-economic, and psychological characteristics to segment the market, in spite of the fact that these are not efficient predictors of future buying behavior of customers. In the present study, benefit segmentation has been used to group retail banking customers in relation to their particular attitudes and behavior. In particular, the identification and measurement of the two major dimensions of service quality, process (how the service is delivered) and outcome (what is delivered) have been investigated to better understand of what constitutes service quality from the customer’s viewpoint. In order to derive... 

Image segmentation in medical imaging, as one of the most important branches of medical image analysis, often faces the challenge of limited labeled data for application in deep learning methods. The high cost of data collection and the need for expertise in image segmentation, particularly in three-dimensional images such as MRI and CT or sequence images like CMR, have all contributed to this problem, even for popular networks like U-Net, which struggle to achieve high accuracy. As a result, research efforts have focused on semi-supervised learning approaches, weakly supervised learning, as well as multi-instance learning in medical image segmentation. Unfortunately, each of these methods... 

One of the most common forms of three-dimensional data is point clouds. In addition to its high flexibility in storing three-dimensional space, this type of data is the closest type of data to the output of three-dimensional sensors. Semantic segmentation of point clouds is a fundamental operation on this type of data, with applications in robotics, self-driving cars, virtual reality, remote sensing, and other fields that work with this type of data. Since deep learning models require abundant data for training, this type of data is not an exception to this rule with these models. However, the problem is that collecting and labeling this type of data is more difficult and costly compared to... 

Geographical data, agricultural field boundaries and their segmentation are essential for many agricultural applications. For example, monitoring of field parcel for resource management. Since manual delineation of land parcels with the help of a real person requires a lot of time and special tools, the need for repeatable automation of this work is felt. Traditional approaches of image segmentation do not have enough generalizability and can be used only for specific areas; so we turned to deep learning, which has proven to be successful in computer vision tasks. Instance segmentation is the most advanced deep learning-based method in object recognition and has numerous applications in... 

In image processing applications, sensors (Camera, LiDAR and Stereo) are essential for scene perception and Deep learning methods outperform most of the image processing tasks like 3D and 2D object detection and semantic segmentation. Different sensors are used in image processing tasks. Sensor fusion is using multiple sensors data to get better performance. Each sensor captures different data (e.g, color, texture, and depth). Some of them are distorted in inclement weather, intense illuminance changes, and dark environments which multi-sensor data fusion is used to overcome sensor weaknesses. One of the most important fields that sensor fusion used is Auto Driving cars (AD). Different... 

Correct diagnosis of myocardial scar has always been a major challenge due to the low resolution of cardiac magnetic resonance imaging. The use of a gadolinium-based contrast agent that reveals a scar is the solution proposed in medical science. However, there are limitations to the use of contrast agents in some patients. In recent years, studies based on deep learning techniques have been presented, trying to identify myocardial infarction with the help of images without using contrast agent. This type of diagnosis can be done with the help of different movements of healthy and damaged tissue. Due to the lack of datasets suitable for this application, in this study, real dataset were... 

Despite recent advancements in deep learning methods, these methods rely on a huge amount of training data to work. Recently the problem of solving classification and recently semantic segmentation problems with a few training data have gained attention to tackle this issue. In this research, we propose a meta-learning method by combining optimization-based and prototypical approaches in which a small portion of parameters are optimized with task-specific initialization. In addition to this and designing other parts of the method, we propose a new approach to use query data as an unlabeled sample to enhance task-specific learning. Alongside the mentioned method, we propose an approach to use... 

Despite widespread applications and high performance of deep neural networks in the fields of computer vision, they have been shown to be vulnerable to adversarial examples. An adversarial example is a perturbated image that the magnitude of its difference with its corresponding natural image is small and yet given such example, the network produces incorrect output. In recent years, many approaches have been proposed to increase the robustness of DNNs against adversarial examples with adversarial training being proposed as the most effective defense measure. Approaches based on adversarial training try to increase the robustness of the network by training on the adversarial examples. One of...