Sharif Digital Repository

Today digital imaging systems have been widely used due to their ease of use and proper costs, but still they suffer from low contrast and resolution. Because of technical limits and expensiveness of hardware, software techniques like super resolution have been used. By super resolution we mean increasing the density of an image’s pixels. Super resolution can be categorized as “single image super resolution” and “multi-image super resolution”. Single image super resolution is applied on a low quality image which has blur and/or noise of environment and imaging system and increases its quality and density to an acceptable level. In multi-image super resolution some auxiliary images captured... 

Localization of electrical activity in the brain is one of the major problems in cognitive science and neuroscience. Indeed, Source localization is the inverse processing procedure on brain signals to estimate the location and position of resources in the human brain. Current technics for neurological imaging is included fMRI، PET، MEG and ERP. These methods is not appropriated to answer the question that when does each of different components of the brain begin their activity. The EEG signals could be useful to eliminate some of limitations of above methods. The problem with EEG signals collected from the skull is that they don’t refer directly to the location of active neurons. The... 

Congestion in Wireless Sensor Networks (WSNs) leads to both packet loss and excessive energy consumption. That is why congestion has to be controlled during the life of a WSN. One of the go well protocols that has tried to tackle congestion in WSNs is PCCP. PCCP utilizes a cross-layer optimization, imposes a hop-by-hop approach and achieves efficient congestion control; however it has got its own shortcomings. Here we have carried out a survey on congestion problem and have proposed a solution called Modified PCCP to elevate the performance of PCCP by making some changes in the scheduler which is designed in the cross layer. Using this model the congestion is controlled and the PCCP protocol... 

Today, psychiatric disorders have turned into a widespread epidemic worldwide. The World Health Organization estimates that one in every four individuals experiences a mental disorder at least once during their lifetime. Mental health problems not only directly affect the health of the affected individuals but also impose significant social and economic costs on the health system and society in terms of diagnosing and treating these disorders. Early, accurate diagnosis and effective treatment of mental health-related disorders can alleviate the suffering of individuals grappling with these illnesses. Machine learning methods are employed as a desirable solution for identifying mental health... 

During recent decades, dramatic advances in Genetics and Molecular Biology, has provided scientists with enormous amounts of molecular genomic information of different living organisms, from DNA sequences to complex 3d structures of proteins. This information is raw data which their analysis can provide better understanding of genome mechanisms, discriminating healthy and tumor cells, predicting disease type, making drugs based on genome information, and many more applications. Here, one important issue is the inevitable use of computer science and statistics to analyze these data; such that according to the vast amount of data, would provide intelligent methods, which yield most accurate... 

Releasing 9 GHz frequency band around 60 GHz frequency in 2005 by Federal Communications Commission (FCC) for industry, scientific and medical (ISM) usage, utilization of this band for production of communication equipments for different short-range wireless applications including cable removal from consumer electronic equipments, ultra high speed file transfer, and high speed data networks, is rapidly spread out. Specific characteristics of this band including most importantly high attenuation because of propagation and intense atmospheric absorption, steerable directional antennas, relatively high phase noise, rapid change of channel with time, and very high speed communication over 25... 

Many studies has been focused on extracting structural and behavioral properties of complex networks in recent decade. Online Social Networks (OSNs) are one example of complex networks. Nowadays with rapid growth of OSNs such as Facebook and Twitter, the study of OSNs has become an interesting research area. Many of recent OSN studies studied friendship networks. Friendship network is a binary unweighted network, and all of its links have the same importance. But, in reality not only all friendship links are not representative of social interactions, but also the social links have a variety of intimacy, intensity, and etc. So, all links should not be considered equal. Recently researchers... 

Feature extraction in subspace domain for face recognition has attracted growing attention in recent years. Face image shown by a long vector usually belongs to a manifold of intrinsically low dimension. Researchers in face recognition field try to extract these manifolds using algebraic and statistical tools. Recently, the use of multilinear algebra and multidimensional data in various stages of feature extraction and recognition is considered. This approach reduces small sample size problem and computational cost by considering the spatial information in the image. Although these successes, the performance of the methods based of this idea in term of recognition rate in the applications... 

Wireless sensor networks (WSNs) consume energy for their sensing, computation, and communication. To extend the lifetime of the network, sensor nodes are equipped with energy storage devices. Recharging their batteries is impossible in most applications. Therefore, energy consumption needs to be monitored and be limited to extend the high performance operation of the network. In this network, the communication module consumes the highest amount of energy. While several methods are proposed to reduce the energy consumption, data compression is one of the most effective ways for energy management by reducing the number of bits to be broadcast. To determine the energy efficiency of the... 

When an object or its background changes, occlusion or shape change occurs, most of the existed methods fail to track the target. To tackle this problem, we want to use sparse representation that has a great power in classification and reconstruction. Sparsity is a typical and practical hypothesis in many spaces. If a signal isn’t sparse in a space, it can be transformed to another space that is sparse in it. Articles that are published on visual tracking using sparse representation show that this field has attracted a lot of interest in the recent years. Here we have proposed two new methods that have reasonable results. Moreover, while it is well known that sparse representation-based... 

Internet of Things means making the all of things smart to access them anytime, anywhere through the Internet. Existing of IoT as a novel way of networking, opens up new issues round the communication and network world. One of them is how to establish the security in such a diffuse network. Because the members of this network are not smart phones, computers or any other usual networking technologies, but they are the normal things and appliances in our daily lives, such as refrigerators. Smart things are mostly resource-constrained and power-limited so the former ways of securing networks cannot be applicable in IoT. Thus, Security bootstrapping has is introduced a solution for establishing... 

Knowing the exact position of the robot in real world is one of crucial and important aspects of its navigation process. For this purpose, several inertial sensors such as gyroscope, accelerometer and compass have been used; however, each one of these sensors has its own drawbacks which cause some inaccuracies in some specific situations. Moreover, the Global Positioning System (GPS) is not available in indoor environments and also not accurate in outdoor places. All of these reasons have persuaded researchers to use camera frames captured from the top of robot as new information for estimating motion parameters of the robot. The main challenge for vision aided localization algorithms is... 

Nowadays, Wireless Sensor Networks (WSNs) play a role in the field of information technology for data gathering purposes. In general, each WSN contains many inexpensive wireless nodes, each capable of collecting, processing, and storing environmental information, and communicating with neighboring nodes. WSNs have been successfully used in different applications such as object tracking, emergency guiding, and environment monitoring. The popular WSN platforms in real-world applications are MICAz, Tmote , and Dust Network. Two standards, namely the ZigBee and IEEE 802.15.4 standards are projected to provide interoperability of different platforms, and to define physical, MAC, and network... 

In recent years, sparse representation has had a variety of applications in computer vision such as noise reduction, image reconstruction, classification and dimension reduction. In this project, we aim to provide a method of matching the keypoints obtained from the Scale Invariant feature Transform (SIFT) algorithm. In this algorithm is used descriptor instead of intensity . The proposed method, first, extracts the salient points from the images and learns a dictionary-based descriptors corresponding to the points. Then, using the dictionary, it obtains the sparse coefficients for each salient point by which, it determines the correspondence of the salient points in the two images using SVD... 

Wireless sensor networks (WSNs) have recently achieved a great deal of attention due to its numerous attractive applications in many different fields. Sensors and WSNs possesses a number of special characteristics that make them very promising in many applications, but also put on them lots of constraints that make issues in sensor network particularly difficult. These issues may include topology control, routing, coverage, security, and data management. In this thesis, we focus our attention on the coverage problem. Firstly, we study the best current solutions in the literature. We choose one of them as the basis of our work and study it thoroughly as the core model of our coverage model.... 

Video segmentation means grouping of pixels of the video sequences into spatio-temporal regions which exhibit coherence in both appearance and motion. Due to complexity and spatio-temporal variations, dynamic texture segmentation is a one of the most challenging task in video processing. The problem of dynamic texture segmentation has received considerable attention due to the explosive growth of its applications in video analysis and surveillance systems. In this thesis, two novel approaches have been proposed. The first proposed method is based on generative Dynamic texture models (DTMs) which represent videos as a linear dynamical system. Since DTMs cannot be used for complex videos which... 

The growth of the knowledge of image processing and machine vision in recent years has led to many applications in various fields. One of the most important applications in machine vision is automotive navigation of vehicles and robots. The effective use of visual sensors to detect obstacles, routing, detecting the position of the robot, and mapping the environment is one of the most important goals in ground robotics. Few methods using sensors such as accelerometers, gyroscopes and global positioning systems, suffer from problems such as high costs, accumulative errors, dependencies on external systems, and the inability to be used in closed spaces. But with the use of the visual sensors,... 

The use of machine learning and deep learning tools to predict the future behavior of trends in massive data requires the extraction and creation of the eigenvector for the chosen model in the problem. It should be noted that simply by increasing the number of features, it cannot be expected that the learning model will have a higher efficiency. Rather, the quality and importance of the features in the field under study should be carefully considered. Topics such as data redundancy, data correlation, the amount of information in the data, distorted data, outliers, etc. are important steps in improving the dataset and creating a feature vector for training the learning model. In the realm of... 

Change detection involves comparing images of a specific area taken at different times to identify the key changes that have occurred over time. It is critically important in the field of remote sensing, with applications spanning urban planning, disaster monitoring, and environmental conservation. However, challenges such as varying weather conditions and lighting in satellite images taken at different times, as well as the presence of non-target changes, can make this task challenging. In this research, we will focus on changes in land-use, particularly in building infrastructure, which is crucial for urban development planning. Moreover, since change detection does not specify the type of... 

Nowadays Wireless Sensor Networks are used in different situations such as greenhouse, agriculture, military applications and etc. The problem of Wireless Sensor Network is that the sensor nodes have limited the energy source. Besides, the power source of sensor nodes is not rechargeable or replaceable. So, one of the main challenges in Wireless Sensor Networks is saving energy. Whereas, most power is consumed due to transmissions, recently a lot of activities have been accomplished in order to reduce transmission times. In this thesis we have focused on a network coding method which is called Sensecode. Sensecode is a technique for reducing communications between nodes. Actually network...