Sharif Digital Repository

The notion of compressibility of a random measure is a rather general concept which find applications in many contexts from data compression, to signal quantization, and parameter estimation. While compressibility for discrete and continuous measures is generally well understood, the case of discrete-continuous measures is quite subtle. In this paper, we focus on a class of multi-dimensional random measures that have singularities on affine lower-dimensional subsets. We refer to this class of random variables as affinely singular. Affinely singular random vectors naturally arises when considering linear transformation of component-wise independent discrete-continuous random variables. To... 

Optical structures can serve as low-power high-capacity alternatives of electronic processors for more efficient neuromorphic computing, but can suffer from large footprints and weak scalability. In this work, properly phased time-perturbed microrings side-coupled to a waveguide are utilized to realize a compact processor for linear transformations. We build up a synthetic frequency dimension to provide sufficient degrees of freedom, where the linear time-varying structures enable the linear intermixing and transformation of frequency-multiplexed data. Moreover, non-reciprocal and asymmetric flow of data in the forward and backward modes, due to phasing of the perturbations, helped to build... 

We present a feasible full step interior-point algorithm to solve the (Formula presented.) horizontal linear complementarity problem defined on a Cartesian product of symmetric cones, which is not based on a usual barrier function. The full steps are scaled utilizing the Nesterov-Todd (NT) scaling point. Our approach generates the search directions leading to the full-NT steps by algebraically transforming the centring equation of the system which defines the central trajectory using the induced barrier of a so-called positive-asymptotic kernel function. We establish the global convergence as well as a local quadratic rate of convergence of our proposed method. Finally, we demonstrate that... 

We consider an index coding problem in which several transmitters deliver distinct files to a number of users with minimum delay. Each user has access to a subset of other files from the library, which can be used as side information. The information sent by the transmitters experience a linear transformation before being received at the users. By benefiting from the concept of Zero-Forcing in MIMO systems, we generalize the notion of MinRank characterization and the clique cover algorithm to accommodate this generalized setting. We show that increasing the number of transmitters can substantially reduce the delivery delay. © 1997-2012 IEEE  

According to the previous studies in the field of economics-oriented cloud resource allocation using game theory, finding an equilibrium point for price is difficult in many cases. This is due to the stochastic situations of the cloud market. So, to tackle this problem we should find a space to describe behavior of the players’ strategies that be independent from the equilibrium point. Therefore, a new algorithm for VM-to-user mapping in the cloud market called VMUMA is proposed. For designing VMUMA, the cloud market is modeled by game theory. VMUMA is based on Sustainable Strategy Space Theory (SSST) in which, each stability and instability of the player’s strategies in the game are defined... 

Due to the automatic feature extraction procedure via multi-layer nonlinear transformations, the deep learning-based visual trackers have recently achieved a great success in challenging scenarios for visual tracking purposes. Although many of those trackers utilize the feature maps from pre-trained convolutional neural networks (CNNs), the effects of selecting different models and exploiting various combinations of their feature maps are still not compared completely. To the best of our knowledge, all those methods use a fixed number of convolutional feature maps without considering the scene attributes (e.g., occlusion, deformation, and fast motion) that might occur during tracking. As a... 

The optical polarization properties of virus-infected cells have been measured, analyzed, and compared with the uninfected cells. In this regard, Vero and HeLa cells have been used as the host for Herpes simplex viruses (HSV). By using polarization microscopy imaging technique, the Mueller matrix images of infected and uninfected cells have been recorded. Through image processing and further analysis, the polarization properties of host cells are compared with their infected ones. For quantitative analysis, the multispectral Mueller matrix transformation (MMT) parameters (A and b) are calculated to identify the microstructural differentiations between uninfected and infected cells by HSV.... 

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.... 

We present a feasible full step interior-point algorithm to solve the (Formula presented.) horizontal linear complementarity problem defined on a Cartesian product of symmetric cones, which is not based on a usual barrier function. The full steps are scaled utilizing the Nesterov-Todd (NT) scaling point. Our approach generates the search directions leading to the full-NT steps by algebraically transforming the centring equation of the system which defines the central trajectory using the induced barrier of a so-called positive-asymptotic kernel function. We establish the global convergence as well as a local quadratic rate of convergence of our proposed method. Finally, we demonstrate that... 

The Renyi's information dimension (RID) of an n-dimensional random vector (RV) is the average dimension of the vector when accounting for non-zero probability measures over lower-dimensional subsets. From an information-theoretical perspective, the RID can be interpreted as a measure of compressibility of a probability distribution. While the RID for continuous and discrete measures is well understood, the case of a discrete-continuous measures presents a number of interesting subtleties. In this paper, we investigate the RID for a class of multi-dimensional discrete-continuous random measures with singularities on affine lower dimensional subsets. This class of RVs, which we term affinely... 

The growing demand for statistical process monitoring has led to the vast utilization of multivariate control charts. Complicated structure of the measured variables associated with highly correlated characteristics, has given rise to daily increasing urge for reliable substitutes of conventional methods. In this regard, projection methods have been developed to address the issue of high correlation among characteristics by transforming them to an uncorrelated set of variables. Principal component analysis (PCA)-based control charts are widely used to overcome the issue of correlation among measured variables by defining linear transformations of the existing variables to a new uncorrelated... 

We recruit basic properties of Fourier transformation to realize an electrically tunable delay line and increase the delay by creating a loop in the structure. The proposed method has been experimentally tested and verified for up to 5 roundtrips. The relative delay/advancement was increased up to 5 times compared to the original setup while preserving the original setup's advantages. © VDE VERLAG GMBH  

This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data sets. This novel framework integrates the state-of-the-art Bayesian formulations into a hierarchical setting aiming to capture both the identification precision and the variability prompted due to modeling errors. Such developments have been absent from the modal identification literature, sustained as a long-standing problem at the research spotlight. Central to this framework is a Gaussian hyper probability model, whose mean and covariance matrix are unknown, encapsulating the uncertainty of the modal parameters.... 

In this study, an algebraic closed-form method for jointly locating the target and refining the antenna positions in multiple-input-multiple-output radar systems is proposed. First, a set of linear equations is formed by non-linear transformation and nuisance parameters elimination, and then, an estimate of the target position is obtained by employing a weighted least-squares estimator. To jointly refine the target and antenna positions, the associated error terms are estimated in the sequence. The proposed method is shown analytically and confirmed by simulations to attain the Cramér-Rao lower bound performance under small-error conditions. Numerical simulations are given to support the... 

An approach based on unitary transformation for the problem of estimating the number of signals is proposed in this study. Among the information theoretic criteria, the authors focus on the conventional Bayesian information criterion (BIC) in the presence of a uniform linear array. The sample covariance matrix of this array is transformed into the real symmetric one by using a unitary transformation. This real symmetric matrix has real eigenvalues and eigenvectors. Therefore its eigenvalue decomposition needs only real computations. Since the eigenvalues of this real symmetric matrix are equal to the eigenvalues of the sample covariance matrix, by replacing them in BIC formula, the term... 

The binary-valued images usually represent shapes. Therefore, the recovery of binary images from samples is often linked with recovery of shapes, where certain parametric structures are assumed on the shape. In this paper, we study the recovery of shape images with the perspective of low-rank matrix recovery. The matrix of such images is not automatically low-rank. Therefore, we consider the Hankel transformation of binary images in order to apply tools in low-rank matrix recovery. We introduce an ADMM technique for the reconstruction which is numerically confirmed to yield suitable results. We also analyze the sampling requirement of this process based on the theory of random matrices. ©... 

The growing industrial needs for the development of strong load-bearing materials by powder metallurgy and casting technologies has led to recent progress in the synthesis of in situ hybrid aluminium matrix composites (AMCs). Unlike their conventional counterparts, this class of engineering materials and their physicomechanical properties are sparsely investigated with no satisfactorily systematic approach and are not reviewed up to now. This is why providing an overview summarizing the formation mechanisms of in situ phases and mechanical properties of hybrid AMCs can systematically guide the research path in this field. The present review strives to categorize hybrid AMCs based on their... 

This paper introduces the concept of on-chip temporal optical computing, based on dispersive Fourier transform and suitably designed modulation module, to perform mathematical operations of interest, such as differentiation, integration, or convolution in time domain. The desired mathematical operation is performed as signal propagates through a fully reconfigurable on-chip photonic signal processor. Although a few numbers of photonic temporal signal processors have been introduced recently, they are usually bulky or they suffer from limited reconfigurability which is of great importance to implement large-scale general-purpose photonic signal processors. To address these limitations, this... 

We present a novel method for deriving the governing equations of the musculoskeletal system, a new class of multibody systems in which the constituent components are connected together via anatomical joints which behave differently compared with traditional mechanical joints. In such systems, the kinematics of the joints and the corresponding constraints are characterized experimentally. We generate the equations of motion of these complex systems in which the homogeneous transformation matrices become matrix-valued functions of the generalized coordinate vector due to the empirical expression of body coordinates as smooth functions of generalized coordinates. The detailed mathematical... 

The growing demand for statistical process monitoring has led to the vast utilization of multivariate control charts. Complicated structure of the measured variables associated with highly correlated characteristics, has given rise to daily increasing urge for reliable substitutes of conventional methods. In this regard, projection methods have been developed to address the issue of high correlation among characteristics by transforming them to an uncorrelated set of variables. Principal component analysis (PCA)-based control charts are widely used to overcome the issue of correlation among measured variables by defining linear transformations of the existing variables to a new uncorrelated...