123 research outputs found
PREPARATION AND PHOTOLUMINESCENCE PROPERTIES OF RF-SPUTTERED ZnO FILMS
ZnO/Si films were prepared by radio frequency (RF) magnetron sputtering at room temperature. By optimizing the heat treatment conditions, we obtained a good quality film annealed at 700 ºC for longer 60 minutes. This process was monitored carefully by Raman scattering spectroscopy, and X-ray diffraction. The photoluminescence study on this film revealed that only ultraviolet emissions due to donor-acceptor pair (DAP), neutral acceptor-bound exciton (AºX) and donor-bound exciton (DºX) were observed. The intensity and peak position of these emissions depend on the measurement temperature and excitation power density
PREPARATION AND PHOTOLUMINESCENCE PROPERTIES OF RF-SPUTTERED ZnO FILMS
ZnO/Si films were prepared by radio frequency (RF) magnetron sputtering at room temperature. By optimizing the heat treatment conditions, we obtained a good quality film annealed at 700 ºC for longer 60 minutes. This process was monitored carefully by Raman scattering spectroscopy, and X-ray diffraction. The photoluminescence study on this film revealed that only ultraviolet emissions due to donor-acceptor pair (DAP), neutral acceptor-bound exciton (AºX) and donor-bound exciton (DºX) were observed. The intensity and peak position of these emissions depend on the measurement temperature and excitation power density
Linear Query Approximation Algorithms for Non-monotone Submodular Maximization under Knapsack Constraint
This work, for the first time, introduces two constant factor approximation
algorithms with linear query complexity for non-monotone submodular
maximization over a ground set of size subject to a knapsack constraint,
and . is a deterministic algorithm
that provides an approximation factor of while is a
randomized algorithm with an approximation factor of . Both run in
query complexity. The key idea to obtain a
constant approximation ratio with linear query lies in: (1) dividing the ground
set into two appropriate subsets to find the near-optimal solution over these
subsets with linear queries, and (2) combining a threshold greedy with
properties of two disjoint sets or a random selection process to improve
solution quality. In addition to the theoretical analysis, we have evaluated
our proposed solutions with three applications: Revenue Maximization, Image
Summarization, and Maximum Weighted Cut, showing that our algorithms not only
return comparative results to state-of-the-art algorithms but also require
significantly fewer queries
Effect of Dzyaloshinskii–Moriya interaction on Heisenberg antiferromagnetic spin chain in a longitudinal magnetic field
Using functional integral method for the Heisenberg antiferromagnetic spin chain with the added Dzyaloshinskii-Moriya Interaction in the presence of the longitudinal magnetic field, we find out expression for free energy of the spin chain via spin fluctuations, from which quantities characterize the antiferromagnetic order and phase transition such as staggered and total magnetizations derived. From that, we deduce the significant effect of the Dzyaloshinskii-Moriya interaction on the reduction of the antiferromagnetic order and show that the total magnetization can be deviated from the initial one under the influence of canting of the spins due to a combination of the Dzyaloshinskii-Moriya interaction and the magnetic field. Besides, the remarkable role of the transverse spin fluctuations due to the above factors on the antiferromagnetic behaviours of the spin chain is also indicated.  
Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP
In video streaming over HTTP, the bitrate adaptation selects the quality of
video chunks depending on the current network condition. Some previous works
have applied deep reinforcement learning (DRL) algorithms to determine the
chunk's bitrate from the observed states to maximize the quality-of-experience
(QoE). However, to build an intelligent model that can predict in various
environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from
these environments must be sent to a server for training centrally. In this
work, we integrate federated learning (FL) to DRL-based rate adaptation to
train a model appropriate for different environments. The clients in the
proposed framework train their model locally and only update the weights to the
server. The simulations show that our federated DRL-based rate adaptations,
called FDRLABR with different DRL algorithms, such as deep Q-learning,
advantage actor-critic, and proximal policy optimization, yield better
performance than the traditional bitrate adaptation methods in various
environments.Comment: 13 pages, 1 colum
Treatment efficiency and membrane fouling of a lab-scale anaerobic membrane bioreactor treating dilute municipal wastewater
The study aims to investigate the application of anaerobic processes coupled with membrane filtration for treating dilute municipal wastewater in Hanoi city
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