Sharif Digital Repository

Deficit in imitation skills is one of the core symptoms of children with Autism Spectrum Disorder (ASD). In this study, we have tried to look closer at the body gesture imitation performance of 20 participants with autism, i.e. ASD group, and 20 typically developing subjects, i.e. TD group, in a set of robot-child and human-child gross imitation tasks. The results of manual scoring by two specialists indicated that while the TD group showed a significantly better imitation performance than the ASD group during the tasks, both ASD and TD groups performed better in the human-child mode than the robot-child mode in our experimental setup. Next, to introduce an automated imitation assessment... 

Deficit in imitation skills is one of the core symptoms of children with Autism Spectrum Disorder (ASD). In this study, we have tried to look closer at the body gesture imitation performance of 20 participants with autism, i.e. ASD group, and 20 typically developing subjects, i.e. TD group, in a set of robot-child and human-child gross imitation tasks. The results of manual scoring by two specialists indicated that while the TD group showed a significantly better imitation performance than the ASD group during the tasks, both ASD and TD groups performed better in the human-child mode than the robot-child mode in our experimental setup. Next, to introduce an automated imitation assessment... 

Automatic recognition of facial expression and facial identity from visual data are two challenging problems that are tied together. In the past decade, researchers have mostly tried to solve these two problems separately to come up with face identification systems that are expression-independent and facial expressions recognition systems that are person-independent. This paper presents a new framework using sparse representation for simultaneous recognition of facial expression and identity. Our framework is based on the assumption that any facial appearance is a sparse combination of identities and expressions (i.e., one identity and one expression). Our experimental results using the CK+... 

Sparse representation is a new approach that has received significant attention for image classification and recognition. This paper presents a PCA-based dictionary building for sparse representation and classification of universal facial expressions. In our method, expressive facials images of each subject are subtracted from a neutral facial image of the same subject. Then the PCA is applied to these difference images to model the variations within each class of facial expressions. The learned principal components are used as the atoms of the dictionary. In the classification step, a given test image is sparsely represented as a linear combination of the principal components of six basic... 

Automatic measurement of spontaneous facial action units (AUs) defined by the facial action coding system (FACS) is a challenging problem. The recent FACS user manual defines 33 AUs to describe different facial activities and expressions. In spontaneous facial expressions, a subset of AUs are often occurred or activated at a time. Given this fact that AUs occurred sparsely over time, we propose a novel method to detect the absence and presence of AUs and estimate their intensity levels via sparse representation (SR). We use the robust principal component analysis to decompose expression from facial identity and then estimate the intensity of multiple AUs jointly using a regression model... 

Automated intensity estimation of spontaneous Facial Action Units (AUs) defined by Facial Action Coding System (FACS) is a relatively new and challenging problem. This paper presents a joint supervised dictionary learning (SDL) and regression model for solving this problem. The model is casted as an optimization function consisting of two terms. The first term in the optimization concerns representing the facial images in a sparse domain using dictionary learning whereas the second term concerns estimating AU intensities using a linear regression model in the sparse domain. The regression model is designed in a way that considers disagreement between raters by a constant biasing factor in... 

Automated measurement of the intensity of spontaneous facial Action Units (AU) defined by the Facial Action Coding System (FACS) in video sequences is a challenging problem. This paper proposes a person-adaptive methodology for the intensity estimation of spontaneous AUs. We formulate this problem as a source separation problem where we consider the observed AUs as the source signals to be separated from each other and other information given by a sequence of facial images. We first compute an initial estimation of the sources, called observations, using sparse linear regression functions. We then develop and apply a Bayesian source separation method that recruits the prior information of... 

Needle insertion simulation and planning systems (SPSs) will play an important role in diminishing inappropriate insertions into soft tissues and resultant complications. Difficulties in SPS development are due in large part to the computational requirements of the extensive calculations in finite element (FE) models of tissue. For clinical feasibility, the computational speed of SPSs must be improved. At the same time, a realistic model of tissue properties that reflects large and velocity-dependent deformations must be employed. The purpose of this study is to address the aforementioned difficulties by presenting a cost-effective SPS platform for needle insertions into the liver. The study... 

In this paper, we study a variation of the equations of a chemotaxis kinetic model and investigate it in one dimension. In fact, we use fractional diffusion for the chemoattractant in the Othmar-Dunbar-Alt system (Othmer in J Math Biol 26(3):263-298, 1988). This version was exhibited in Calvez in Amer Math Soc, pp 45-62, 2007 for the macroscopic well-known Keller-Segel model in all space dimensions. These two macroscopic and kinetic models are related as mentioned in Bournaveas, Ann Inst H Poincaré Anal Non Linéaire, 26(5):1871-1895, 2009, Chalub, Math Models Methods Appl Sci, 16(7 suppl):1173-1197, 2006, Chalub, Monatsh Math, 142(1-2):123-141, 2004, Chalub, Port Math (NS), 63(2):227-250,... 

In this study, the corrosion behaviour of Ni-Co alloys with low Co content, electroplated on steel substrate in sulphate bath, was investigated. The morphology of coatings was studied by optical and SEM microscopy. The corrosion products were analyzed using EDX. The results showed that Ni-1%Co coatings had a better corrosion resistance 0.30, 0.92 and 3.75 mpy for atmospheric, salt spray and polarization tests, respectively. These are 0.41, 1.20 and 5.40 mpy for pure nickel coatings that indicate the least corrosion resistance. Surface analysis revealed the presence of oxides, sulphides and chlorides in corrosion products  

The flight of unmanned aerial vehicles is often associated with model uncertainties, measurement noises, and environmental disturbances such as wind gust. To mitigate these challenges, the accurate estimation of states is vital. Moreover, the wind model and its parameters should also be estimated and compensated during the flight. In this paper, a multiple model filter is implemented for this purpose. To investigate the performance of the multiple model filter, three different models including constant wind, '1-cosine' model and wind shear model are considered. The multiple model filter utilizes three extended Kalman filter to simultaneously estimate the model of wind, the parameters of the... 

In this paper, a new nonlinear robust adaptive impedance controller is addressed for Unmanned Aerial Vehicles (UAVs) equipped with a robot manipulator that physically interacts with environment. A UAV equipped with a robot manipulator is a novel system that can perform different tasks instead of human being in dangerous and/or inaccessible environments. The objective of the proposed robust adaptive controller is control of the UAV and its robotic manipulators end-effector impedance in Cartesian space in order to have a stable physical interaction with environment. The proposed controller is robust against parametric uncertainties in the nonlinear dynamics model of the UAV and the robot... 

A new method for fabrication of silver nano-islands on glass fibers using a top-down process is introduced. A thin layer of silver chemically coated on the surface of the glass fibers evolves into silver islands by heat treatment. The effect of the concentration of the initial solution and the temperature were investigated. Segregation was more clearly observed for lower solution concentrations and higher temperatures. The temperature of 500 °C was found optimum where separated islands form. At higher temperatures, the coagulation and burial of silver islands occur. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and diffused reflectance spectroscopy (DRS) were... 

In this paper, a nonlinear robust adaptive sliding mode control strategy is presented for the influenza epidemics in the presence of model uncertainties. The nonlinear epidemiological model of influenza with five state variables (the numbers of susceptible, exposed, infected, asymptomatic and recovered individuals) and two control inputs (vaccination and antiviral treatment) is considered. The objective of the proposed controller is decreasing the number of susceptible and infected humans to zero by tracking the desired scenarios. As a result of this decreasing, the number of exposed and asymptomatic individuals is also decreased and converged to the zero. Accordingly, it is shown that the... 

A new composite adaptive control strategy is developed for both of the reduction of cancer tumor volume and the online identification of tumor parameters during the drug delivery process in chemotherapy. This control strategy is developed for three different nonlinear mathematical cell-kill models of the cancer tumor including the log-kill hypothesis, Norton-Simon hypothesis and Emax hypothesis. All of these models are considered to have fully parametric uncertainties. The stability, tracking convergence and parameters identification convergence during the chemotherapy process are proved using the Lyapunov method. For the first time, the parameters identification of the uncertain... 

To have accurate control of a wind turbine, especially when facing wind loads, it is vital to model the dynamic behavior of the system accurately and select the controller parameters properly. To mitigate these challenges, here, an optimal model-based controller is utilized using particle swarm optimization to suppress the vibrations of the tower for an NREL 5 MW turbine in the presence of the turbulence wind. For this purpose, first, a discrete state-space model is derived for the wind turbine based on the finite difference method, and the validity of the model structure is verified with its analytical solution. Then, the structure of a model predictive controller is implemented on the... 

The accurate control of a gas turbine usually requires precise information of its nonlinear model, as well as all states and the parameters. To access the information of the model, an accurate estimation and compensation of the variables of the gas turbine in the controller architecture are vital. In this study, an adaptive model-based controller using the synergetic approach is utilized to control the generated power and exhaust temperature for a heavy-duty gas turbine power generator unit. For this purpose, first, the problem of the controllability of the nonlinear gas turbine is studied. Then, performance of the proposed controller is compared with a classical PI and well-known nonlinear...