Sharif Digital Repository

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... 

In this study, various methods of speech time scale modification are inspected and the most important one is modified. First, a brief review on all existing methods for audio time scale modification and its applications is presented in introduction section. As will be noted, the presented method could be classified in three classes of Time domain, Phase-vocoder and signal model. In next chapter a review on Time-domain methods is presented and its advantages and disadvantages have been addressed. Afterward, a review on Phase-vocoder and Signal-model methods is expressed in chapter 3 and 4 respectively. Considering the complexity and extent material relating to these techniques, the research... 

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... 

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  

Most existing face recognition methods require several gallery (training) images per person for optimally training the system. On the other hand, in many real applications of face recognition such as law enforcement, driver’s license and identification by passport, generally only one image per person is available. However, “the variations between face images with different illumination and gestures are almost always larger than image variations due to change in face identity”, Therefore, in this thesis we have considered face recognition with changes in ambient lighting conditions and viewing angles where only one training image per person is availabe. A method have been proposed in which we... 

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... 

Data Oriented Smart Monitoring for Industrial Machineries include approaches for fault detection and prognosis which only rely on non-stationary signals sampled from sensors and do not rely on physical model of machineries nor expert knowledge. Fault detection is task of determining state of machinery in present moment using past data. But in Prognosis focus is on predicting future state of machinery using past data. Most researches in this category are based on supervised algorithms, but in many applications labeling data is expensive. In this thesis some approaches for semi-superviseddiagnosis, based on markov random walk an K-NN have been implemented, also some improvements for K-NN have... 

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... 

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... 

Nowadays there is an immense volume of websites containing valuable content. These diverse and unorganized contents are most usable, when stripped to their gist and put together as a whole. The main purpose of this project is to provide a system which extracts the core content of these websites and puts them all together, organized and fashioned for potential users. More specifically the main process devised for this project is to extract the desired data out of the websites, contents according to the Train Data fed to machine learning algorithms as input. The very input that is determined by the user and mandates which data should be distinguished out of websites as the output. For... 

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... 

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... 

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... 

Simultaneous development of technology and social networks and universal access to them caused to produce and distribute huge volume of videos that recognition of their content without use of machine vision is very hard. This thesis examine some video classification algorithms to improve them. The algorithm that is used to improve is based on one of local descriptor algorithms. At first with using STIP tools, the local interest point found by Harris3d and describe by HOG/HOF. Then by using Bag of Features, all local descriptors in a video produce a descriptor per videos. Bag of Features divide the domain of all local descriptors from all videos to K cluster and produce a vector per video... 

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... 

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... 

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...