Sharif Digital Repository

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class... 

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class... 

We study the quantum plasmonic features of gold and silver nanoparticles using TD-DFT+TB, a new density functional theory approach to the calculation of excited states, which combines a full DFT ground state with tight-binding approximations in the linear response calculation. In this framework, the optical properties of closed-shell Ag, Au and bimetallic Ag-Au nanoparticles with tetrahedral symmetry (with 20, 56, 120, and 165 atoms) and icosahedral structure (with 13, 55, and 147 atoms) were obtained and compared to full linear response time-dependent density functional theory (TD-DFT) as a reference and also to time-dependent density functional based tight binding (TD-DFTB) as a low-cost... 

Metal nanoclusters can be synthesized in various sizes and shapes and are typically protected with ligands to stabilize them. These ligands can also be used to tune the plasmonic properties of the clusters as the absorption spectrum of a protected cluster can be significantly altered compared to the bare cluster. In this paper, we computationally investigate the influence of thiolate ligands on the plasmonic intensity for silver, gold and alloy clusters. Using time-dependent density functional theory with tight-binding approximations, TD-DFT+TB, we show that this level of theory can reproduce the broad experimental spectra of Au144(SR)60 and Ag53Au91(SR)60 (R = CH3) compounds with... 

Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they... 

The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research. © 2017 IEEE  

The unique plasmonic character of silver and gold nanoparticles has a wide range of applications, and tailoring this property by changing electronic and geometric structures has received a great deal of attention. Herein, we study the role of the quantum properties in controlling the plasmonic excitations of gold and silver atomic chains and rods. The influence of relativistic effects, scalar as well as spin-orbit, on the intensity and energy of plasmonic excitations is investigated. The intensity quenching and the red shift of energy in the presence of relativistic effects are introduced via the appearance of d orbitals directly in optical excitations in addition to the screening of... 

Hand gesture recognition (HGR) from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of the human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of keyframes of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatiotemporal... 

Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal... 

In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional... 

Improvement of mechanical properties and microstructure of 2024 aluminum alloy by performing a severe plastic deformation method, called multidirectional forging (MDF), and heat treatment was the aim of this research. In this work, the effects of different heat treatments such as annealing, solid solution, peak ageing and over ageing before MDF on mechanical properties and microstructure of the alloy were studied. Microstructure evolution during severe plastic deformation was considerably affected by the precipitates and non-shearable particles. On the other hand, the severe plastic deformation had different effects on precipitates in the microstructure. Fragmentation, scattering and... 

Purpose - The aim of this paper is to look at the relationship between research in Iranian medical universities, together with their Web Impact Factor (WIF). Design/methodology/approach - The Altavista search engine was chosen because of its advanced search facilities for counting links and having a wider scope of academic web sites than other search engines. It was searched for determining the number of pages, in-links and self-links of 42 web sites of Iranian medical universities. The Web Impact Factor (WIF) was calculated by two formulas and the relationship between the two grades of universities (WIF and ISI) was calculated by using the Spearman's correlation coefficient. Findings -... 

Improvement of mechanical properties and microstructure of 2024 aluminum alloy by performing a severe plastic deformation method, called multidirectional forging (MDF), and heat treatment was the aim of this research. In this work, the effects of different heat treatments such as annealing, solid solution, peak ageing and over ageing before MDF on mechanical properties and microstructure of the alloy were studied. Microstructure evolution during severe plastic deformation was considerably affected by the precipitates and non-shearable particles. On the other hand, the severe plastic deformation had different effects on precipitates in the microstructure. Fragmentation, scattering and... 

The effect of particle size on the catalytic activity of H-ZSM-5 zeolite in the methanol to propylene (MTP) reaction was investigated in a fixed-bed flow reactor under the operating conditions of T = 460 °C, P = 1 atm, and WHSV = 1 h-1. Nano and micro size H-ZSM-5 were prepared by reflux and hydrothermal crystallization methods, respectively. The nano and micro H-ZSM-5 were characterized using XRD, NH3-TPD, BET area, SEM and ICP-AES analytical techniques. Nano size H-ZSM-5 showed higher activity and stability compared to the micro size H-ZSM-5. Nano H-ZSM-5 was also found to have higher selectivity to propylene than the micro size H-ZSM-5. © 2009 Elsevier B.V. All rights reserved  

Material properties and fracture characteristics are among the most prominent parameters that should be considered for a wide range of graphene applications. This article reviews recent advances in theoretical studies on the mechanical properties and fracture behaviors of graphene, focusing on the effect of various simulation models. Most studies investigated single-layer graphene sheets (SLGSs) under uniaxial tensile tests using different common interatomic potentials, particularly AIREBO. Although researchers have examined a similar problem, specifically for pristine graphene, the differences in the reported values are considerable. These discrepancies are most evident in fracture... 

The peridynamic (PD) theory is a nonlocal reformulation of mechanics with various advantages over common approaches, mainly local continuum mechanics and molecular dynamics (MD). PD theory can capture phenomena at different dimensions, including nanoscale. However, limited studies have been performed by this theory in nanoscale, which have generally focused on the feasibility and accuracy of using PD in atomic-scale modeling. In the present study, based on the ordinary state-based peridynamic method, we investigate the fracture of pre-cracked single layer graphene sheets (SLGSs) under uniaxial tension. By simulating the exact atomic model of graphene, the failure strain and crack growth... 

For a wide range of graphene applications, it is required to examine the fracture characteristics of single-layer graphene sheets. In this article, we study the effective parameters in fracture of graphene, concentrating on the impact of atomistic modeling on results that have not been adequately evaluated in previous studies. We considered two distinct models to simulate a uniaxial tensile test in molecular dynamics. By comparing these models, we explore the influence of various parameters on the results, particularly fracture strength and failure strain. We demonstrate that in pristine graphene sheets, failure depends entirely on simulation modeling. The two main factors that lead to these... 

Soft errors due to cosmic radiations are the main reliability threat during lifetime operation of digital systems. Fast and accurate estimation of soft error rate (SER) is essential in obtaining the reliability parameters of a digital system in order to balance reliability, performance, and cost of the system. Previous techniques for SER estimation are mainly based on fault injection and random simulations. In this paper, we present an analytical SER modeling technique for ASIC designs that can significantly reduce SER estimation time while achieving very high accuracy. This technique can be used for both combinational and sequential circuits. We also present an approach to obtain... 

This paper presents a new method for restoration of distribution networks after a fault occurrence. This problem is solved from the viewpoint of the distribution system operator with the main goal of minimizing the operating cost during the fault clearance period. The effects of distributed generation (DG) units and direct load control (DLC) programs are considered in designing the proposed restoration procedure. Moreover, the uncertainties associated with the predicted loads of different nodes and the availability of DG are modeled here. Robust optimization is used to model the uncertainties of restoration problems and manage their associated risks. Finally, a robust reconfiguration plan is...