Sharif Digital Repository

In this paper, a fast and accurate algorithm for identifying circular objects in images is proposed. The presented method is a robust, fast and optimized adaption of Circular Hough Transform (CHT), Eigenvalues of Covariance Matrix and K-means clustering techniques. Results are greatly improved by implementing iterative K-means clustering algorithm and establishing an exponential growth instead of updating values in the parameter space of CHT through summation, both in runtime and quality. In fact, using the Eigenvalues of Covariance Matrix as a validating method, a well balanced compromise between the speed and accuracy of results is achieved. This method is tested on several real world... 

The different characteristics of UWSN and trade off between UWSN and WSN, have been discussed in many researches. Here, we aim to propose a new architecture for very large scale underwater sensor network. In deployment part of sensors, topology plays a crucial role in issues like communication performance, power consumption, network reliability and fault tolerance capabilities. Hence, it is so critical and should be analyzed how we deploy sensors in targets environment. For instance, to improve reliability of our networks in harsh conditions, it is so important to avoid deploying underwater sensors with single point of failure and bottleneck. For this purpose, we present enhanced clustering... 

Success in forecasting and analyzing sales for given goods or services can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore, reliable prediction of sales becomes a very important task. This article presents a novel sales forecasting approach by the integration of genetic fuzzy systems (GFS) and data clustering to construct a sales forecasting expert system. At first, all records of data are categorized into k clusters by using the K-means model. Then, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K-means... 

Fault tolerance is an essential requirement in systems running applications which need a technique to continue execution where some system components are subject to failure. In this paper, a fault tolerant task scheduling algorithm is proposed for mapping task graphs to heterogeneous processing nodes in cluster computing systems. The starting point of the algorithm is a DAG representing an application with information about the tasks. This information consists of the execution time of the tasks on the target system processors, communication times between the tasks having data dependencies, and the number of the processor failures (ε) which should be tolerated by the scheduling algorithm. The... 

Surface electromyogram (sEMG) signals, a noninvasive bioelectric signal, can be used for the rehabilitation and control of artificial extremities. Current sEMG pattern-recognition systems suffer from a limited number of patterns that are frequently intensified by the unsuitable accuracy of the instrumentation and analytical system. To solve these problems, we designed a multistep-based sEMG pattern-recognition system where, in each step, a stronger more capable relevant technique with a noticeable improved performance is employed. In this paper, we utilized the sEMG signals to classify and recognize six classes of hand movements. We employed an adaptive neurofuzzy inference system (ANFIS) to... 

Wireless sensor networks include small nodes which have sensing ability; calculation and connection extend themselves everywhere soon. Such networks have source limitation on connection, calculation and energy consumption. So, since the nodes have limited energy in sensor networks, the optimized energy consumption in these networks is of more importance and has created many challenges. The previous works have shown that by organizing the network nodes in a number of clusters, the energy consumption could be reduced considerably. So the lifetime of the network would be increased. In this paper, we used the Queen-bee algorithm to create energy efficient clusters in wireless sensor networks.... 

The k-nearest neighbor (k-NN) is a widely used classification technique and has significant applications in various domains. The most challenging issues in the k-nearest neighbor algorithm are high dimensional data, the reasonable accuracy of results and suitable computation time. Nowadays, using parallel processing and deploying many-core platforms like GPUs is considered as one of the popular approaches to improving these issues. In this paper, we present a novel and accurate parallel implementation of k-NN based on Mahalanobis distance metric in GPU platform. We design and implement k-NN for GPU architecture and utilize mathematic and algorithmic techniques to eliminate repetitive... 

Wireless sensor networks (WSNs) comprise a large number of small sensor nodes scattered across limited geographical areas. The nodes in such networks carry sources of limited and mainly unchangeable energy. Therefore, it is necessary that these networks operate under energy efficient protocols and structures. Energy efficient clustering algorithms have been developed to reduce the networks energy consumption and extend its lifetime. This paper presents an innovative cluster-based communication protocol for WSNs. In order to reduce communication overhead, the authors propose an Energy Aware Clustering Hierarchy Protocol that creates a multi-level hierarchical structure to adequately route and... 

Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. The k-means algorithm is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. The Fashion Algorithm is one effective method for searching problem space to find a near optimal solution. This paper presents a hybrid optimization algorithm based on Generalized Fashion Algorithm... 

Operational and planning studies of high-wind penetrated power systems have well come to the light as a major concern of future energy systems. This paper focuses on the procedure of determining required static reserve of the high-wind penetrated power systems which has not been well accompanied by comprehensive analysis and proper modeling tools. To reach this goal, first, a probabilistic algorithm has been proposed to effectively model the variations in output generation of wind turbines. In this algorithm, the fuzzy c-means clustering method (FCM) is exploited as an efficient as well as robust clustering method to find the multi-state model of wind turbines output generation. Based on... 

Privacy preservation is an important issue in data publishing. Existing approaches on privacy-preserving data publishing rely on tabular anonymization techniques such as k-anonymity, which do not provide appropriate results for aggregate queries. The solutions based on graph anonymization have also been proposed for relational data to hide only bipartite relations. In this paper, we propose an approach for anonymizing multirelation constraints (ternary or more) with (t,k) hypergraph anonymization in data publishing. To this end, we model constraints as undirected hypergraphs and formally cluster attribute relations as hyperedge with the t-means-clustering algorithm. In addition,... 

A good approach in data mining is density based clustering in which the clusters are constructed based on the density of shape regions. The prominent algorithm proposed in density based clustering family is DBSCAN [1] that uses two global density parameters, namely minimum number of points for a dense region and epsilon indicating the neighborhood distance. Among others, one of the weaknesses of this algorithm is its un-suitability for multi-density data sets where different regions have various densities so the same epsilon does not work. In this paper, a new density based clustering algorithm, MSDBSCAN, is proposed. MSDBSCAN uses a new definition for core point and dense region. The... 

Energy constraint is the most critical problem in wireless sensor networks (WSNs). To address this issue, clustering has been introduced as an efficient way for routing. However, the available clustering algorithms do not efficiently consider the geographical information of nodes in cluster-head election. This leads to uneven distribution of cluster-heads and unbalanced cluster sizes that brings about uneven energy dissipation in clusters. In this paper, an Efficient Distributed Cluster-head Election technique for Load balancing (EDCEL) is proposed. The main criterion of the algorithm, dispersal of cluster-heads, is achieved by increasing the Euclidian distance between cluster-heads.... 

In this paper an off-line ArabiclFarsi handwritten recognition Algorithm on a subset of Farsi name is proposed. In this system, There is no sub-word segmentation phase. Script database includes 3300 images of 30 Farsi common names. The features are wavelet coefficients extracted from smoothed word image profiles in four standard directions. The Centers of competitive layer of RBF neural network have been determined by combining GA and K-Means clustering algorithm. Weights of supervised layer has been trained by using LMS rule and the distances of feature vector of each sample to the centre of RBF network have been computed based on warping function. Experimental results show advantages of... 

Color quantization is a technique for processing and reduction colors in image. The purposes of color quantization are displaying images on limited hardware, reduction use of storage media and accelerating image sending time. In this paper a hybrid algorithm of GA and Particle Swarm Optimization algorithms with FCM algorithm is proposed. Finally, some of color quantization algorithms are reviewed and compared with proposed algorithm. The results demonstrate Superior performance of proposed algorithm in comparison with other color quantization algorithms  

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all... 

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known... 

Wireless sensor networks are tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy-consuming operation. It thus becomes essential to increase the lifetime of a WSN by minimizing the number of bits sent by each device. One well known approach is to aggregate sensor data. Data aggregation is used to eliminate redundancy and minimize the number of transmissions in order to save energy. Most energy efficient aggregation protocols have focused on cluster-based structure approaches. In this paper, we investigate the efficiency of data aggregation by focusing on two aspects of the problem: first how to improve cluster-based routing... 

Data aggregation is a technique that is used to decrease extra and repetitive data in cluster based routing protocols. As we know SPEED routing algorithm is based on service quality and dose not perform data aggregation. In this article, we try to add an data aggregation technique to the available techniques without interfering with the functions of previous ones. The idea involves virtual configuration of sensors and specification of an individual ID to the created data by the sensors in each region, then data aggregation in relay node is done by this ID, resulting in less energy consumption, lower traffic and repeated data, an increase in network lifetime and better quality of service  

Clustering-based image segmentation is a well-known multispectral image segmentation method. However, as it inherently does not account for the spatial relation among image pixels, it often results in inhomogeneous segmented regions. The recently proposed membership-connectedness (MC)-based segmentation method considers the local and global spatial relations besides the fuzzy clustering stage to improve segmentation accuracy. However, the inherent spatial and intraclass redundancies in multispectral images might decrease the accuracy and efficiency of the method. This letter addresses these two problems and proposes a segmentation method that is based on the MC method, watershed transform,...