Sharif Digital Repository

This paper examines active selection of clustering constraints, which has become a topic of significant interest in constrained clustering. Active selection of clustering constraints, which is known as minimizing the cost of acquiring constraints, also includes quantifying utility of a given constraint set. A sequential method is proposed in this paper to select the most beneficial set of constraints actively. The proposed method uses information of boundary points and transition regions extracted by data description methods to introduce a utility measure for constraints. Since previously selected constraints affect the utility of remaining candidate constraints, a method is proposed to... 

In this paper, we address the problem of constrained clustering along with active selection of clustering constraints in a unified framework. To this aim, we extend the improved possibilistic c-Means algorithm (IPCM) with multiple kernels learning setting under supervision of side information. By incorporating multiple kernels, the limitation of improved possibilistic c-means to spherical clusters is addressed by mapping non-linear separable data to appropriate feature space. The proposed method is immune to inefficient kernels or irrelevant features by automatically adjusting the weight of kernels. Moreover, extending IPCM to incorporate constraints, its strong robustness and fast... 

Learning an appropriate distance measure under supervision of side information has become a topic of significant interest within machine learning community. In this paper, we address the problem of metric learning for constrained clustering by considering three important issues: (1) considering importance degree for constraints, (2) preserving the topological structure of data, and (3) preserving some natural distribution properties in the data. This work provides a unified way to handle different issues in constrained clustering by learning an appropriate distance measure. It has modeled the first issue by injecting the importance degree of constraints directly into an objective function.... 

In clustering spatial data, we are given a set of points in Rn and the objective is to find the clusters (representing spatial objects) in the set of points. Finding clusters with different shapes, sizes, and densities in data with noise and potentially outliers is a challenging task. This problem is especially studied in machine learning community and has lots of applications. We present a novel clustering technique, which can solve mentioned issues considerably. In the proposed algorithm, we let the structure of the data set itself find the clusters, this is done by having points actively send and receive feedbacks to each other. The idea of the proposed method is to transform the input... 

Image segmentation is one of the fundamental problems in image processing and computer vision, since it is the first step in many image analysis systems. This paper presents a new perspective to image segmentation, namely, segmenting input images by applying efficient community detection algorithms common in social and complex networks. First, a common segmentation algorithm is used to fragment the image into small initial regions. A weighted network is then constructed. Each initial region is mapped to a vertex, and all these vertices are connected to each other. The similarity between two regions is calculated from colour information. This similarity is then used to assign weights to the... 

The goal of image segmentation is to find regions that represent objects or meaningful parts of objects. In this paper a new method is presented for color image segmentation which involves the ideas used for community detection in social networks. In the proposed method an initial segmentation is applied to partition input image into small homogeneous regions. Then a weighted network is constructed from the regions, and a community detection algorithm is applied to it. The detected communities represent segments of the image. A remarkable feature of the method is the ability to segments the image automatically by optimizing the modularity value in the constructed network. The performance of... 

This paper presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of... 

In recent years, processing the images that contain human faces has been a growing research interest because of establishment and development of automatic methods especially in security applications, compression, and perceptual user interface. In this paper, a new method has been proposed for multiple face detection and tracking in video frames. The proposed method uses skin color, edge and shape information, face detection, and dynamic movement analysis of faces for more accurate real-time multiple face detection and tracking purposes. One of the main advantages of the proposed method is its robustness against usual challenges in face tracking such as scaling, rotation, scene changes, fast... 

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin... 

One of the main challenges in diffusion-based molecular communication is dealing with the non-linearity of reaction-diffusion chemical equations. While numerical methods can be used to solve these equations, a change in the input signals or the parameters of the medium requires one to redo the simulations. This makes it difficult to design modulation schemes and practically impossible to prove the optimality of a given transmission strategy. In this paper, we provide an analytical technique for modeling the non-linearity of chemical reaction equations based on the perturbation method. The perturbation method expresses the solution in terms of an infinite power series. An approximate solution... 

One of the main challenges in diffusion-based molecular communication is dealing with the non-linearity of reaction-diffusion chemical equations. While numerical methods can be used to solve these equations, a change in the input signals or the parameters of the medium requires one to redo the simulations. This makes it difficult to design modulation schemes and practically impossible to prove the optimality of a given transmission strategy. In this paper, we provide an analytical technique for modeling the non-linearity of chemical reaction equations based on the perturbation method. The perturbation method expresses the solution in terms of an infinite power series. An approximate solution... 

The differential equations describing reaction-diffusion systems in molecular communication are non-linear and do not admit closed-form solutions in general. Consequently, characterizing optimal modulation and coding strategies is a key challenge in molecular communication. In this paper, we consider a concrete example and illustrate the application of tools from real and complex analysis to study this difficult problem. We show that the optimal release pattern is a finite sum of Dirac's delta functions in our working example. Simulation results validate the discrete nature of the optimal release pattern. We also provide an upper bound on the numbers of delta functions under some... 

In this paper, we propose a new method to reconstruct a signal corrupted by noise where both signal and noise are sparse but in different domains. The main contribution of our algorithm is its low complexity; it has much lower run-time than most other algorithms. The reconstruction quality of our algorithm is both objectively (in terms of PSNR and SSIM) and subjectively better or comparable to other state-of-the-art algorithms. We provide a cost function for our problem, present an iterative method to find its local minimum, and provide the analysis of the algorithm. As an application of this problem, we apply our algorithm for Salt-and-Pepper noise (SPN) and Random-Valued Impulsive Noise... 

In this paper, we propose a novel algorithm that combines color and texture information of skin with cellular learning automata to segment skin-like regions in color images. First, the presence of skin colors in an image is detected, using a committee structure, to make decision from several explicit boundary skin models. Detected skin-color regions are then fed to a color texture extractor that extracts the texture features of skin regions via their color statistical properties and maps them to a skin probability map. Cellular learning automatons use this map to make decision on skin-like regions. The proposed algorithm has demonstrated true positive rate of about 83.4% and false positive... 

A novel intelligent semi-active control system for an eleven degrees of freedom passenger car's suspension system using magnetorheological (MR) damper with neuro-fuzzy (NF) control strategy to enhance desired suspension performance is proposed. In comparison with earlier studies, an improvement in problem modeling is made. The proposed method consists of two parts: a fuzzy control strategy to establish an efficient controller to improve ride comfort and road handling (RCH) and an inverse mapping model to estimate the force needed for a semi-active damper. The fuzzy logic rules are extracted based on Sugeno inference engine. The inverse mapping model is based on an artificial neural network... 

Using CNG as an additive for gasoline is a proper choice due to higher octane number of CNG enriched gasoline with respect to that of gasoline. As a result, it is possible to use gasoline with lower octane number in the engine. This would also mean the increase of compression ratio in SI engines resulting in higher performance and lower gasoline consumption. Over the years, the use of simulation codes to model the thermodynamic cycle of an internal combustion engine have developed tools for more efficient engine designs and fuel combustion. In this study, a thermodynamic cycle simulation of a conventional four-stroke spark-ignition engine has been developed. The model is used to study the... 

With the declining energy resources and increase of pollutant emissions, a great deal of efforts has been focused on the development of alternatives for fossil fuels. One of the promising alternative fuels to gasoline in the internal combustion engine is natural gas [1-5]. The application of natural gas in current internal combustion engines is realistic due to its many benefits. The higher thermal efficiency due to the higher octane value and lower exhaust emissions including CO2 as a result of the lower carbon to hydrogen ratio of the fuel are the two important feature of using CNG as an alternative fuel. It is well known that computer simulation codes are valuable economically as a cost... 

Gaining insight into possible vibratory responses of dynamical systems around their stable equilibria is an essential step, which must be taken before their design and application. The results of such a study can significantly help prevent instability in closed-loop stabilized systems by avoiding the excitation of the system in the neighborhood of its resonance. This paper investigates nonlinear oscillations of a rotary inverted pendulum (RIP) with a full-state feedback controller. Lagrange’s equations are employed to derive an accurate 2-DoF mathematical model, whose parameter values are extracted by both the measurement and 3D modeling of the real system components. Although the governing...