Sharif Digital Repository

The flow behaviour of Al–SiC nanocomposites prepared by mechanical milling and hot powder extrusion methods was studied at different temperatures (350–500°C) and strain rates (0.005–0.5 s−1). The flow of the Powder metallurgy nanocomposites exhibited a peak stress followed by a dynamic flow softening behaviour. It was shown that mechanical milling increased high-temperature strain rate sensitivity of ultrafine-grained (UFG) aluminium while decreasing its flow dependence to temperature. Constitutive analysis of the hot deformation process by Zener–Hollomon parameter (Z) also indicated a remarkable increase in the deformation activation energy (about 40%). Likewise, SiC nanoparticles (up to... 

Event-related potential (ERP) extraction from ongoing electroencephalograph (EEG) and its enhancement is one of the long-established problems in EEG signal processing. Most of the previous studies have focused mainly on the ERP enhancement without considering the multi-dimentional structure of the signal. In order to take advantage of this property, we propose a tensor-based solution with trial-by-trial concatenated ERP data. Then we develop an algorithm based on low-rank Tucker decomposition to detect single trial ERP component with maximized signal to noise ratio (SNR). In other words, by using tensor algebra we consider both self-similarity in intratrials and global correlation in spatial... 

Since low-cost RFID systems are applied in ubiquitous varied applications, privacy and security of their users became a great concern. Therefore, the various authentication protocols have been proposed. In this paper, we inspect the three new-found RFID authentication protocols based on quadratic residue property via one of the well-organized formal RFID privacy models instead of intuitive analysis. We formally prove that modular squaring is the suitable technique to guarantee RFID authentication protocols against backward traceability. Then, the flaws are alleviated to resist traceability attacks  

Conventional buffet onset methods for a 2D supercritical airfoil, SC0410, in transonic regime for various Mach number and various angles of attack have been surveyed. The existing methods give good results for high subsonic and transonic regimes, but demand a computational procedure to detect the buffet onset. One of these methods, trailing edge pressure divergence, that have been recognized inappropriate in other studies for supercritical airfoils, shows acceptable result at least for the present supercritical airfoil. A new method has been proposed by the authors for transonic regime that is based on the physical definition of the buffet onset from the surface pressure distribution... 

The effect of a magnetic field on the fully developed forced convection of Fe3O4 flow inside a copper tube is experimentally and numerically investigated. The flow is assumed to be under uniform heat flux. This study aims to examine the effects of the nanoparticle volume fraction, as well as alternating magnetic field strength and frequency, on the convective heat transfer for different Reynolds numbers. To ensure accuracy, the numerical results are validated by empirical results with similar geometry and boundary conditions. A satisfying agreement was achieved. The results show that the heat transfer increases with increase in alternating magnetic field frequency but... 

Carbon nanotubes (CNTs) were dispersed without any solvent in poly(tetramethylene ether glycol), (PTMEG) well above its melting point by ultrasonication in the pulse mode and different times. The polyol/CNT suspensions were used to prepare in situ polymerized thermoplastic polyurethane TPU/CNT nanocomposites with the CNT concentration of ∼ 0.05 vol%, much below the CNT geometrical percolation threshold calculated at 0.43 vol%. Results of rotational rheological measurements and ultraviolet-visible (UV-Vis) spectroscopy analysis revealed improvement in the nanoscale CNT dispersion with sonication time. Moreover, the optical microscopic images and sedimentation behavior for these samples... 

This paper proposes a new framework for corrective voltage control (CVC) of power systems. It ensures a desired loading margin (LM) after encountering severe contingencies while minimizing the corresponding control costs. The framework is divided into primary CVC (PCVC) and secondary CVC (SCVC) stages for restoration of voltage stability and ensuring a desired LM. These stages are based on the sequence and quickness of the control actions required in post-contingency state of the system. The PCVC sub problem deals with the condition faced by a power system subject to voltage instability as the result of severe contingencies. Such control is merely devised to restore system stability. Next,... 

This paper proposes a new framework for corrective voltage control (CVC) of power systems. It ensures a desired loading margin (LM) after encountering severe contingencies while minimizing the corresponding control costs. The framework is divided into primary corrective voltage control (PCVC) and secondary CVC (SCVC) stages for restoration of voltage stability and ensuring a desired LM. These stages are based on the sequence and quickness of the control actions required in post-contingency state of the system. The PCVC sub problem deals with the condition faced by a power system subject to voltage instability as the result of severe contingencies. Such control is merely devised to restore... 

Hand pose estimation is one of the most attractive research areas for image processing. Among the human body parts, hands are particularly important for human–machine interactions. The advent of commercial depth cameras along with the rapid growth of deep learning has made great progress in all image processing fields, especially in hand pose estimation. In this study, using depth data, we introduce two hybrid deep neural networks to estimate 3D hand poses with fewer computations and higher accuracy compared with their counterparts. Due to the fact that the dimensions of data are reduced while passing through successive layers of networks, which causes data to be lost, we use the concept of... 

Hand pose estimation is one of the most attractive research areas for image processing. Among the human body parts, hands are particularly important for human–machine interactions. The advent of commercial depth cameras along with the rapid growth of deep learning has made great progress in all image processing fields, especially in hand pose estimation. In this study, using depth data, we introduce two hybrid deep neural networks to estimate 3D hand poses with fewer computations and higher accuracy compared with their counterparts. Due to the fact that the dimensions of data are reduced while passing through successive layers of networks, which causes data to be lost, we use the concept of...