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Tran Hoang Hai,Le Hong Phuc,Doan Thi Kim Dung,Nguyen Thi Le Huyen,Bui Duc Long,Le Khanh Vinh,Nguyen Thi Thanh Kieu,Massanori Abe 한국물리학회 2008 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.53 No.2
Magnetic resonance imaging (MRI) has become a major and promising topic in medical researching because of its numerous potential applications. Due to the specic uptake by macrophage and not entirely captured by liver and spleen at first-pass, Superparamagnetic nanoparticles (SPIONs) are widely investigated as diagnostic tracer for magnetic resonance imaging (MRI). Surfactantcoated Fe3O4 particles (6 nm diameter) have been synthesized by using a wet chemical method (co-precipitation). Our study concentrated on synthesizing magnetic nanoparticles Fe3O4 coating oleic acid and Dextran and Starch polysaccharides.
Approximated dielectric tensor of the biaxial α-SnSe crystal
Le Long Van,Nguyen Hoang Tung,Kim Tae Jung,Nguyen Xuan Au,Kim Young Dong 한국물리학회 2021 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.78 No.4
We present the principal components of the dielectric tensor for α-SnSe biaxial single crystal in the spectral range from 0.74 to 6.42 eV at room temperature (300 K) using spectroscopic ellipsometry. Aspnes’ first-order approximation method was applied to obtain approximated pseudodielectric response from the pseudodielectric tensor measured at an angle of incidence of ϕ = 68.8°. Multilayer calculations were then performed to extract the pure dielectric tensor without surface roughness artifacts. The Tauc–Lorentz model was applied to describe the critical point structures, and the optical constants along the principal axes of biaxial α-SnSe single crystal can be now calculated at arbitrary wavelength for device applications.
Optimal Data Scheduling and Admission Control for Backscatter Sensor Networks
Hoang, Dinh Thai,Niyato, Dusit,Wang, Ping,Kim, Dong In,Bao Le, Long Institute of Electrical and Electronics Engineers 2017 IEEE Transactions on Communications Vol. No.
<P>This paper studies the data scheduling and admission control problem for a backscatter sensor network (BSN). In the network, instead of initiating their own transmissions, the sensors can send their data to the gateway just by switching their antenna impedance and reflecting the received RF signals. As such, we can reduce remarkably the complexity, the power consumption, and the implementation cost of sensor nodes. Different sensors may have different functions, and data collected from each sensor may also have a different status, e.g., urgent or normal, and thus we need to take these factors into account. Therefore, in this paper, we first introduce a system model together with a mechanism in order to address the data collection and scheduling problem in the BSN. We then propose an optimization solution using the Markov decision process framework and a reinforcement learning algorithm based on the linear function approximation method, with the aim of finding the optimal data collection policy for the gateway. Through simulation results, we not only show the efficiency of the proposed solution compared with other baseline policies, but also present the analysis for data admission control policy under different classes of sensors as well as different types of data.</P>
QoS-Aware and Energy-Efficient Resource Management in OFDMA Femtocells
Long Bao Le,Niyato, D.,Hossain, E.,Dong In Kim,Dinh Thai Hoang IEEE 2013 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.12 No.1
<P>We consider the joint resource allocation and admission control problem for Orthogonal Frequency-Division Multiple Access (OFDMA)-based femtocell networks. We assume that Macrocell User Equipments (MUEs) can establish connections with Femtocell Base Stations (FBSs) to mitigate the excessive cross-tier interference and achieve better throughput. A cross-layer design model is considered where multiband opportunistic scheduling at the Medium Access Control (MAC) layer and admission control at the network layer working at different time-scales are assumed. We assume that both MUEs and Femtocell User Equipments (FUEs) have minimum average rate constraints, which depend on their geographical locations and their application requirements. In addition, blocking probability constraints are imposed on each FUE so that the connections from MUEs only result in controllable performance degradation for FUEs. We present an optimal design for the admission control problem by using the theory of Semi-Markov Decision Process (SMDP). Moreover, we devise a novel distributed femtocell power adaptation algorithm, which converges to the Nash equilibrium of a corresponding power adaptation game. This power adaptation algorithm reduces energy consumption for femtocells while still maintaining individual cell throughput by adapting the FBS power to the traffic load in the network. Finally, numerical results are presented to demonstrate the desirable operation of the optimal admission control solution, the significant performance gain of the proposed hybrid access strategy with respect to the closed access counterpart, and the great power saving gain achieved by the proposed power adaptation algorithm.</P>