Sharif Digital Repository

In this paper, a target parameter estimation problem is addressed for the recently emerging frequency diverse array multiple-input multiple-output (FDA-MIMO) radar system, utilizing sparse learning. The scene is modeled as a two dimensional (2D) angle-incremental range grid. To solve the resulting sparse problem, the recently proposed user-parameter free algorithms including block sparse learning via iterative minimization (BSLIM), iterative adaptive approach (IAA), sparse iterative covariance-based estimation (SPICE), likelihood-based estimation of sparse parameters (LIKES), and orthogonal matching pursuit (OMP) are applied which achieve excellent parameter estimation performance. However,... 

We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing... 

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting... 

This brief proposes a joint graph signal recovery and topology learning algorithm using a Variational Bayes (VB) framework in the case of non-Gaussian measurement noise. It is assumed that the graph signal is Gaussian Markov Random Field (GMRF) and the graph weights are considered statistical with the Gaussian prior. Moreover, the non-Gaussian noise is modeled using two distributions: Mixture of Gaussian (MoG), and Laplace. All the unknowns of the problem which are graph signal, Laplacian matrix, and the (Hyper)parameters are estimated by a VB framework. All the posteriors are calculated in closed forms and the iterative VB algorithm is devised to solve the problem. The efficiency of the... 

Reinforcement learning methods such as Q-learning have shown promising results in the model-free design of linear quadratic regulator (LQR) controllers for linear time-invariant (LTI) systems. However, challenges such as sample-efficiency, sensitivity to hyper-parameters, and compatibility with classical control paradigms limit the integration of such algorithms in critical control applications. This paper aims to take some steps towards bridging the well-known classical control requirements and learning algorithms by using optimization frameworks and properties of conic constraints. Accordingly, a new off-policy model-free approach is proposed for learning the Q-function and designing the... 

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performance of deep neural networks in different applications of image processing and computer vision, we present an end-to-end fully convolutional neural network to remove impulsive noise from images. To train our network, we generate a customized dataset with various noise densities in which the highly corrupted images are more frequent. Hence, our convolutional neural network is blind since the percentage of impulsive noise is not required as... 

In this paper, the vulnerability of large-dimensional dynamic networks to false data injections is analysed. The malicious data can manipulate input injection at the control nodes and affect the outputs of the network. The objective is to analyse and quantify the potential vulnerability of the dynamics by such adversarial inputs when the opponents try to avoid being detected as much as possible. A joint set of most effective actuation nodes and most vulnerable target nodes are introduced with minimal detectability by the monitoring system. Detection of this joint set of actuation-target nodes is carried out by introducing a Gramian-based measure and reformulating the vulnerability problem as... 

Two-degree-of-freedom (2-DOF) electrical machines require position sensors for their motion control. In comparison with using two independent sensors, using a 2-DOF sensor enhances the closed-loop control system's performance. However, due to the 3-D structure of the 2-DOF sensor, its performance evaluation needs 3-D analysis. Also, due to helical motion the accuracy deterioration of the sensor, under mechanical faults needs more attention. Although the finite element method (FEM) is the best way to simulate such sensors, most of the commercial packages for transient finite element simulations are not able to consider two separate motions simultaneously. Furthermore, FEM has a high... 

This paper studies the real-time energy management of large-scale residential households and standalone electric vehicles charging stations using a non-cooperative game based on consensus protocol. We consider a set of aggregators, each equipped with a processor, to minimize its own cost function by having access to the local estimation terms of neighboring aggregators. Since the cost function of each aggregator is affected by strategy of other aggregators through total generation cost, such interaction among competitive agents is modeled as a non-cooperative game. An idea based on model predictive control is utilized to deal with highly random behavior of users. In this paper, a time-of-use... 

The heat transfer simulations of turbine blades with internal cooling are faced with so many uncertainties, of which some originate from the secondary air system, including the inlet hot gas temperature and pressure and the cooling side boundary conditions, and the blade material. The main objective of this work is to carry out a suitable sensitivity analysis on a specific novel turbine vane to improve the thermal performance of its internal cooling system and to quantify how the uncertainties on the designed/calculated values can desirably/undesirably affect the maximum blade surface temperature, which can consequently affect the gas turbine engine efficiency. Furthermore, the sensitivity... 

In this paper, the assignability of linear time-invariant (LTI) systems with respect to arbitrary control structures is addressed. It is well established that the closed-loop spectrum of an LTI system with an arbitrary control structure is confined to the set containing the fixed-modes of the system with respect to that control structure. However, the assignment of the closed-loop spectrum is not merely limited by the existence of fixed-modes in practical scenarios. The pole assignment may require excessive control effort or even become infeasible due to the presence of small perturbations in the system dynamics. To offer more insights in such more realistic scenarios, a continuous measure... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this article, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforce the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network (CDNN) to fulfill the data diversity. Outcomes... 

In this paper, the optimal trade-off between control structures and achievable closed-loop performance is addressed. Incorporation of sparsity promoting regularization terms to the primary objective function is a well-suited approach in feature selection and compressed sensing. By the evolving role of distributed and large-scale applications, modern optimal control problems have been equipped with regularization tools as well. However, the system dynamics and convex/nonconvex constraints in optimal control framework limits the effectiveness and applicability of regularization, enforce iterative or non-convex heuristics, and pose extensive exploration. In fact, available regularized feedback... 

In this paper, the assignability of linear time-invariant (LTI) systems with respect to arbitrary control structures is addressed. It is well established that the closed-loop spectrum of an LTI system with an arbitrary control structure is confined to the set containing the fixed-modes of the system with respect to that control structure. However, the assignment of the closed-loop spectrum is not merely limited by the existence of fixed-modes in practical scenarios. The pole assignment may require excessive control effort or even become infeasible due to the presence of small perturbations in the system dynamics. To offer more insights in such more realistic scenarios, a continuous measure... 

This paper investigates the physical layer security (PLS) design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Considering the hardware impairments (HIs), both total power budget constraint for the whole network and the individual power constraint at each node, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aiming at maximizing average secrecy rate (ASR). To... 

This paper studies an iterative stochastic algorithm for solving the stochastic shortest path problem. This algorithm, which uses a distributed learning automata, tries to find the shortest path by taking a sufficient number of samples from the edges of the graph. In this algorithm, which edges to be sampled are determined dynamically as the algorithm proceeds. At each iteration of this algorithm, a distributed learning automata used to determine which edges to be sampled. This sampling method, which uses distributed learning automata, reduces the number of samplings from those edges, which may not be along the shortest path, and resulting in a reduction in the number of the edges to be... 

In recent studies, several methods have been suggested to decrease noise of magnetic resonance image (MRI) in order to raise the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). In this paper, we propose a novel method based on a minimization problem in Ripplet domain that uses singular value decomposition (SVD) in low rank learning to eliminate the noise of MRI images. We reschedule the weighted nuclear norm minimization (WNNM) problem in any edges of Ripplet domain transform and using an adaptive weighting structure to denoise the patches of Ripplet component matrix. The parameters of the proposed method are divided into two groups, some of them are calculated... 

Companies depend on mining data to grow their business more than ever. To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required. The lack of flexible and accurate performance models, however, render this a challenging task. This paper fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs). In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios. We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based Big... 

In this study, the SD-HACNEM (Sharif Dynamic - High order Average Current Nodal Expansion Method) neutron noise simulator in two energy groups using a second-order flux expansion method for two-dimensional rectangular X Y-geometry has been developed. In the first step, the calculations were performed for the steady state and results of ACNEM (Average Current Nodal Expansion Method) and HACNEM (High order Average Current Nodal Expansion Method) were examined and compared. To solve the problem, the power iteration algorithm has been used to calculate the distribution of neutron flux and neutron multiplication factor by considering the coarse-mesh (each fuel assembly one node). To validate the... 

In recent years, visual tracking methods that are based on discriminative correlation filters (DCFs) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although a wide range of recent DCF-based methods exploit the features that are extracted from deep convolutional neural networks (CNNs) in their translation model, the scale of the visual target is still estimated by hand-crafted features. Whereas the exploitation of CNNs imposes a high computational burden, this paper exploits pre-trained lightweight CNNs models to propose two efficient scale estimation methods, which not only improve the visual tracking performance but also...