Sharif Digital Repository

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... 

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... 

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.... 

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... 

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... 

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... 

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... 

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... 

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... 

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... 

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... 

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,... 

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 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... 

The purpose of this project is estimating the two-or three-dimensional human condition using existing data(images or video). Human pose estimation can be used in applications، including the detection of human behavior، animation،human computer interaction، physical therapy and، etc . We use sparse representation method to estimating human pose In this project .Sparse representation methods in recent years has been used in many fields and probably in pose estimation can achieve good results with this method  

With the advancement of technology, 3D scanners accuracy and information from scanning objects were gained in importance. One of their uses is the augmentation of scanning results in augmented reality or 3D printing. The number of points is too much and in some cases it also reaches several million points. With the development of technology, scanners are improved to have higher accuracy, and the number of points in result generated by them is growing more. This sets of scanned points are called "Point Cloud".The mesh-based methods are commonly used to display these points. These methods are very sensitive to problems encountered such as noise and outlier datas. It also has a very high... 

The importance of transfer learning in image-related problems comes from its many advantages that are sometimes undeniable. Previous researches have well shown the success of transfer learning in this area using deep neural networks. However, transfer learning for time series data has not yet been done in a conventional and automated manner. The main reason for avoiding transfer learning in this domain relates to the dynamic and stochastic nature of the time series, where they show a time-varying behavior. Previous experiments have shown that transfer learning between two heterogeneous time series could harm the forecasting accuracy of a model. Therefore, in this thesis, we aim to explore... 

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...