Evaporation and Evolution of Suspended Solution Droplets at Atmospheric and Reduced Pressures

Thermal history and solute precipitation behavior of suspended solution droplets of sodium chloride (NaCl), magnesium sulphate (MgSO₄), and zirconium hydroxychloride (ZrO(OH)Cl) evaporating at atmospheric and reduced pressures are studied. Experimental measurements on the variation of droplet diameter, solution concentration, and temperature during the evaporation period are presented and discussed. The results of solute precipitation behavior in solution droplets observed under an optical microscope are displayed and discussed. Results indicate that reducing the pressure (∼ 33 kPa) results in a change in the solution droplet evaporation rate, but the thermal histories of a particular solution droplet are similar at the atmospheric and reduced pressures. At atmospheric and reduced pressures used in this study, the d² law for solution droplets is valid at early stages of the evaporation and before the solute precipitation initiates. Drying of MgSO₄ and ZrO(OH)Cl solution droplets results in the formation of spherical particles, whereas drying of spherical NaCl solution droplets results in the formation of cubic particles.     

A novel open-loop high-speed CMOS sample-and-hold

A new open-loop high-speed CMOS sample-and-hold is presented. Based on new method for further reduction of voltage-dependent charge injection, a new CMOS sample-and-hold was designed. Simulation results confirm the effectiveness of this method. Over 10 dB improvement in signal-to-noise ratio, compared to the signal-to-noise ratio of conventional bottom plate sampling S/Hs was achieved with this method. A comparison between newly designed S/H and the bottom-plate sampling S/H is presented.     

Reserve estimation of an open pit mine under price uncertainty by real option approach

Reserve estimation is a key to find the correct NPV in a mining project. The most important factor in reserve estimation is the metal price. Metal price fluctuations in recent years were exaggerated, and imposed a high degree of uncertainty to the re-serve estimation, and in consequence to the whole mine planning procedure. Real option approach is an efficient method of deci-sion making in the uncertain conditions. This approach has been used for evaluation of defined natural resources projects until now. This study considering the metal price uncertainty used real option approach to prepare a methodology for reserve estimation in open pit mines. This study was done on a copper cylindrical deposit, but the achieved methodology can be adjusted for all kinds of deposits. This methodology was comprehensively described through the examples in such a manner that can be used by the mine planners.     

Training based, moving digital filter method for real time heat flux function estimation

In this paper the neural networks is utilized to estimate the “filter coefficients” needed to estimate heat flux in a particular system. In developing the training phase of the network inspiration is drawn from the Burgraff's exact solution of the IHCP as well as the filter method. Thus, the estimation phase neither requires any temperature field nor the sensitivity coefficients calculations. The neural network used in this work is a 2-layer perceptron. It is shown via classical triangular heat flux test cases that the method can yield very accurate, very efficient as well as stable estimations.     

CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors

Reliability of the current microprocessor technology is seriously challenged by radiation-induced soft errors. Accurate Vulnerability Factor (VF) modeling of system components is crucial in designing cost-effective protection schemes in high-performance processors. Although Statistical Fault Injection (SFI) techniques can be used to provide relatively accurate VF estimations, they are often very time-consuming. Unlike SFI techniques, recently proposed analytical models can be used to compute VF in a timely fashion. However, VFs computed by such models are inaccurate as the system-level impact of soft errors is overlooked.     

Machine learning based optimized live virtual machine migration over WAN links

Live virtual machine migration is one of the most promising features of data center virtualization technology. Numerous strategies have been proposed for live migration of virtual machines on local area networks. These strategies work perfectly in their respective domains with negligible downtime. However, these techniques are not suitable to handle live migration over wide area networks and results in significant downtime. In this paper we have proposed a Machine Learning based Downtime Optimization (MLDO) approach which is an adaptive live migration approach based on predictive mechanisms that reduces downtime during live migration over wide area networks for standard workloads. The main contribution of our work is to employ machine learning methods to reduce downtime. Machine learning methods are also used to introduce automated learning into the predictive model and adaptive threshold levels. We compare our proposed approach with existing strategies in terms of downtime observed during the migration process and have observed improvements in downtime of up to 15 %.     

Design of a lightweight and anonymous authenticated key agreement protocol for wireless body area networks

Wireless body area networks (WBANs) are a network designed to gather critical information about the physical conditions of patients and to exchange this information. WBANs are prone to attacks, more than other networks, because of their mobility and the public channel they use. Therefore, mutual authentication and privacy protection are critical for WBANs to prevent attackers from accessing confidential information of patients and executing undetectable physical attacks. In addition, in the authentication and key agreement process, messages should be transferred anonymously such that they are not linkable. In this paper, we first indicate that one of the most recently introduced authentication protocol is vulnerable to the wrong session key agreement attack and desynchronization attack. Second, we propose a lightweight authentication and key agreement protocol, which can withstand the well‐known attacks and provide the anonymity feature. Eventually, we analyze the security of our proposed protocol using both Automated Validation of Internet Security Protocols and Applications (AVISPA) and random oracle model and compare its performance with the related works. The results demonstrate the superiority of our proposed protocol in comparison with the other protocols.     

Optimal characterization of a microwave transistor using grey wolf algorithms

Analog Integrated Circuits and Signal Processing - Modern time microwave stages require low power consumption, low size, low-noise amplifier (LNA) designs with high-performance measures. These...