Sharif Digital Repository

This paper presents a state of the art review of adsorption models for a new plant-based surfactant adsorption onto sandstone minerals. The adsorption data at both kinetic and equilibrium modes were obtained from batch experiments. Four adsorption kinetic models, five two-parameter, and six three-parameter equilibrium models were used for interpretation of the obtained data. Among the two and three-parameter isotherm models applied, the Jovanovic and the Khan isotherms showed the best fit, respectively. And the pseudo-second order model presented a better fit than other kinetic models. Finally, a computer-based modeling approach was developed and used for predicting the kinetics of... 

Recent studies have demonstrated that high-level semantics in data can be captured using sparse representation. In this paper, we propose an approach to human body pose estimation in static images based on sparse representation. Given a visual input, the objective is to estimate 3D human body pose using feature space information and geometrical information of the pose space. On the assumption that each data point and its neighbors are likely to reside on a locally linear patch of the underlying manifold, our method learns the sparse representation of the new input using both feature and pose space information and then estimates the corresponding 3D pose by a linear combination of the bases... 

Grammar induction is a basic step in natural language processing. Based on the volume of information that is used by different methods, we can distinguish three types of grammar induction method: supervised, unsupervised, and semi-supervised. Supervised and semisupervised methods require large tree banks, which may not currently exist for many languages. Accordingly, many researchers have focused on unsupervised methods. Unsupervised Data Oriented Parsing (UDOP) is currently the state of the art in unsupervised grammar induction. In this paper, we show that the performance of UDOP in free word order languages such as Persian is inferior to that of fixed order languages such as English. We... 

Many real-world systems and applications such as World Wide Web, and social interactions can be modeled as networks of interacting nodes. However, in many cases, one encounters the situation where the pattern of the node-to-node interactions (i.e., edges) or the structure of a network is unknown. We address this issue by studying the Network Reconstruction Problem: Given a network with missing edges, how is it possible to uncover the network structure based on certain observable quantities extracted from partial measurements? We propose a novel framework called CS-NetRec based on a newly emerged paradigm in sparse signal recovery called Compressive Sensing (CS). The results demonstrate that... 

The current multi-channel P2P video streaming architectures still suffer from several performance problems such as low Quality of Service (QoS) in unpopular channels. The P2P systems are inherently dynamic, and their performance problems could be categorized into four groups; peer churn, channel churn, uncooperative peers, and geographical distribution of peers. In this paper, for the first time, we develop a novel locality-incentive framework for multi-channel live video streaming. We propose a hierarchical overlay network architecture by utilizing a dual-mode locality-Awareness method (spatial and temporal). Moreover, an incentive mechanism for encouraging peers to dedicate their upload... 

Data streams are endless flow of data produced in high speed, large size and usually non-stationary environments. The main property of these streams is the occurrence of concept drifts. Using decision trees is shown to be a powerful approach for accurate and fast learning of data streams. In this paper, we present an incremental regression tree that can predict the target variable of newly incoming instances. The tree is updated in the case of occurring concept drifts either by altering its structure or updating its embedded models. Experimental results show the effectiveness of our algorithm in speed and accuracy aspects in comparison to the best state-of-the-art methods  

The analysis of core composition changes is complicated by the fact that the time and spatial variation in isotopic composition depend on the neutron flux distribution and vice versa. Fortunately, changes in core composition occur relatively slowly and hence the burnup analysis can be performed by dividing the burnup period into some burnup spans and assuming that the averaged flux and cross sections are constant during each step. The burnup span sensitivity analysis attempts to find that how much the burnup spans could be increased without any significant deviation in results. This goal has been achieved by developing a new MCNP-ORIGEN linkage program named as MOBC (MCNP-ORIGEN Burnup... 

Drawing an accurate relationship between settling time and the power consumption of the amplifier is a challenging problem in Switch Capacitor circuits especially when it includes non-linear effects. In this paper, a new method for the estimation of this relationship including both non-linear settling as a result of slew-rate and small signal settling in the 3 rd order amplifier is proposed. The results show that the proposed settling time estimation is more accurate than other conventional methods when it is compared with the circuit level simulations. The proposed method has error smaller than 10% for the third order OpAmp in estimating settling error. This is about two times more accurate... 

Human detection is one of the hard problems in object detection field. There are many challenges like variation in human pose, different clothes, non-uniform illumination, cluttered background and occlusion which make this problem much harder than other object detection problems. Defining good features, which can be robust to this wide range of variations, is still an open issue in this field. To overcome this challenge, in this paper we proposed a new set of hybrid features. We combined the Histogram of Oriented Gradient (HOG) with the new features called Histogram of Small Edges (HOSE) which is introduced in this paper. These two kinds of features have two different approaches for... 

This paper introduces a novel coding scheme based on the diffusion map framework. The idea is to run a t-step random walk on the data graph to capture the similarity of a data point to the codebook atoms. By doing this we exploit local similarities extracted from the data structure to obtain a global similarity which takes into account the nonlinear structure of the data. Unlike the locality-based and sparse coding methods, the proposed coding varies smoothly with respect to the underlying manifold. We extend the above transductive approach to an inductive variant which is of great interest for large scale datasets. We also present a method for codebook generation by coarse graining the data... 

A large number of Wireless Sensor Network (WSN) security schemes have been proposed in the literature, relying primarily on symmetric key cryptography. To enable those, Random Key pre-Distribution (RKD) systems have been widely accepted. However, WSN nodes are vulnerable to physical compromise. Capturing one or more nodes operating with RKD would give the adversary keys to compromise communication of other benign nodes. Thus the challenge is to enhance resilience of WSN to node capture, while maintaining the flexibility and low-cost features of RKD. We address this problem, without any special-purpose hardware, proposing a new and simple idea: key splitting. Our scheme does not increase... 

In this paper, we propose an optimization framework to retrieve an optimal group of experts to perform a given multi-aspect task/project. Each task needs a diverse set of skills and the group of assigned experts should be able to collectively cover all required aspects of the task. We consider three types of multiaspect team formation problems and propose a unified framework to solve these problems accurately and efficiently. Our proposed framework is based on Facility Location Analysis (FLA) which is a well known branch of the Operation Research (OR). Our experiments on a real dataset show significant improvement in comparison with the state-of-the art approaches for the team formation... 

The prime shortcoming of Wireless Sensor Networks (WSNs) is their energy constraint. The main energy consumer in a sensor node is its radio transmitter. One of the most effective methods to reduce the data transmission rate is data prediction. By data prediction, the amount of transmitted data is reduced; which results in energy saving and the longevity of the network life. Environmental variations almost have similar effects on different sensor sources in a sensor device. So, considering the correlation between different sources reduces the noise impact and increases data prediction accuracy. In this paper, temporal and multi-source correlations are used, to reduce data transmission in... 

In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of... 

In this letter, a new adaptive Gaussian process (GP) frame work for signal extrapolation is proposed. Signal extrapolation is an essential task in many applications such as concealment of corrupted data in image and video communications. While possessing many interesting properties, Gaussian process priors with inappropriate stationary kernels may create extremely blurred edges in concealed areas of the image. To address this problem, we propose adaptive non-stationary kernels in a Gaussian process framework. The proposed adaptive kernel functions are defined based on the hypothesized edges of the missing areas. Experimental results verify the effectiveness of the proposed method compared to... 

This study presents a method based on minimum description length criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the sample covariance matrix (SCM) of Gaussian processes. The authors use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, the authors assume a Marcenko-Pastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces.... 

Process-centered Software Engineering Environments, or PSEEs, are intended for the definition, modification, and enactment of software process models, they thus bring software development processes into effect. Even though research efforts in process-centered software engineering abound, PSEE technology has not received the attention that it deserves. In order to create a concise but effective and practically applicable evaluation framework for PSEEs, this paper first presents a survey of PSEEs and highlights the current state of the art of the technology. The PSEEs which have been reviewed herein have been regarded as software systems, and as such, have been characterized in terms of their... 

Graph based methods are among the most active and applicable approaches studied in semi-supervised learning. The problem of neighborhood graph construction for these methods is addressed in this paper. Neighborhood graph construction plays a key role in the quality of the classification in graph based methods. Several unsupervised graph construction methods have been proposed that have addressed issues such as data noise, geometrical properties of the underlying manifold and graph hyper-parameters selection. In contrast, in order to adapt the graph construction to the given classification task, many of the recent graph construction methods take advantage of the data labels. However, these... 

Cobalt is the most common binder in cemented carbides industry. However, there are some interests in use of alternatives. The similarity in properties has made nickel the first choice. In the present work, the effect of initial composition on modern hardmetals containing transition metal carbides/carbonitrides that are called "cemented carbonitrides" with nickel as binder was investigated. Change in quantity of additive carbides and tungsten to carbon (C/W) weight ratio through applying metallic tungsten powder in primary powder mixture had some effects on final hardness, transverse rupture strength, and microstructure of studied alloys. Addition of vanadium carbide not more than 0.2 wt.%,... 

Since that heuristic state space planners have been very successful in classical planning, this approach is currently the most popular strategy in dealing with temporal planning, too. However, all current state-space temporal planners use a search method known as decision epoch planning, which is not complete for problems with required concurrency. In theory, this flaw can be overcome by employing another search method, called temporally lifted progression planning. In this paper, we show that there are two major problems which, if not tackled properly, can cause the latter method to be very inefficient in practice. The first problem is dealing with the remarkably large state space of...