3,405 research outputs found
Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration
Vehicular fog computing (VFC) has been envisioned as a promising paradigm for
enabling a variety of emerging intelligent transportation systems (ITS).
However, due to inevitable as well as non-negligible issues in wireless
communication, including transmission latency and packet loss, it is still
challenging in implementing safety-critical applications, such as real-time
collision warning in vehicular networks. In this paper, we present a vehicular
fog computing architecture, aiming at supporting effective and real-time
collision warning by offloading computation and communication overheads to
distributed fog nodes. With the system architecture, we further propose a
trajectory calibration based collision warning (TCCW) algorithm along with
tailored communication protocols. Specifically, an application-layer
vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable
distribution with real-world field testing data. Then, a packet loss detection
mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories
based on received vehicle status including GPS coordinates, velocity,
acceleration, heading direction, as well as the estimation of communication
delay and the detection of packet loss. For performance evaluation, we build
the simulation model and implement conventional solutions including cloud-based
warning and fog-based warning without calibration for comparison. Real-vehicle
trajectories are extracted as the input, and the simulation results demonstrate
that the effectiveness of TCCW in terms of the highest precision and recall in
a wide range of scenarios
AI for CSI Feedback Enhancement in 5G-Advanced
The 3rd Generation Partnership Project started the study of Release 18 in
2021. Artificial intelligence (AI)-native air interface is one of the key
features of Release 18, where AI for channel state information (CSI) feedback
enhancement is selected as the representative use case. This article provides
an overview of AI for CSI feedback enhancement in 5G-Advanced. Several
representative non-AI and AI-enabled CSI feedback frameworks are first
introduced and compared. Then, the standardization of AI for CSI feedback
enhancement in 5G-advanced is presented in detail. First, the scope of the AI
for CSI feedback enhancement in 5G-Advanced is presented and discussed. Then,
the main challenges and open problems in the standardization of AI for CSI
feedback enhancement, especially focusing on performance evaluation and the
design of new protocols for AI-enabled CSI feedback, are identified and
discussed. This article provides a guideline for the standardization study of
AI-based CSI feedback enhancement.Comment: 8 pages, 4 figures, 2 table. This work has been submitted to the IEEE
for possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Identifying online user reputation of user–object bipartite networks
Identifying online user reputation based on the rating information of the user–object bipartite networks is important for understanding online user collective behaviors. Based on the Bayesian analysis, we present a parameter-free algorithm for ranking online user reputation, where the user reputation is calculated based on the probability that their ratings are consistent with the main part of all user opinions. The experimental results show that the AUC values of the presented algorithm could reach 0.8929 and 0.8483 for the MovieLens and Netflix data sets, respectively, which is better than the results generated by the CR and IARR methods. Furthermore, the experimental results for different user groups indicate that the presented algorithm outperforms the iterative ranking methods in both ranking accuracy and computation complexity. Moreover, the results for the synthetic networks show that the computation complexity of the presented algorithm is a linear function of the network size, which suggests that the presented algorithm is very effective and efficient for the large scale dynamic online systems
Demonstration of Einstein-Podolsky-Rosen Steering with Enhanced Subchannel Discrimination
Einstein-Podolsky-Rosen (EPR) steering describes a quantum nonlocal
phenomenon in which one party can nonlocally affect the other's state through
local measurements. It reveals an additional concept of quantum nonlocality,
which stands between quantum entanglement and Bell nonlocality. Recently, a
quantum information task named as subchannel discrimination (SD) provides a
necessary and sufficient characterization of EPR steering. The success
probability of SD using steerable states is higher than using any unsteerable
states, even when they are entangled. However, the detailed construction of
such subchannels and the experimental realization of the corresponding task are
still technologically challenging. In this work, we designed a feasible
collection of subchannels for a quantum channel and experimentally demonstrated
the corresponding SD task where the probabilities of correct discrimination are
clearly enhanced by exploiting steerable states. Our results provide a concrete
example to operationally demonstrate EPR steering and shine a new light on the
potential application of EPR steering.Comment: 16 pages, 8 figures, appendix include
Electric-field induced magnetic-anisotropy transformation to achieve spontaneous valley polarization
Valleytronics has been widely investigated for providing new degrees of
freedom to future information coding and processing. Here, it is proposed that
valley polarization can be achieved by electric field induced magnetic
anisotropy (MA) transformation. Through the first-principle calculations, our
idea is illustrated by a concrete example of monolayer. The
increasing electric field can induce a transition of MA from in-plane to
out-of-plane by changing magnetic anisotropy energy (MAE) from negative to
positive value, which is mainly due to increasing magnetocrystalline anisotropy
(MCA) energy. The out-of-plane magnetization is in favour of spontaneous valley
polarization in . Within considered electric field range,
is always ferromagnetic (FM) ground state. In a certain
range of electric field, the coexistence of semiconductor and out-of-plane
magnetization makes become a true ferrovalley (FV)
material. The anomalous valley Hall effect (AVHE) can be observed under
in-plane and out-of-plane electrical field in . Our works
pave the way to design the ferrovalley material by electric field.Comment: 6 pages, 6 figures. arXiv admin note: text overlap with
arXiv:2207.1342
Janus monolayer ScXY (XY=Cl, Br and I) for piezoelectric and valleytronic application: a first-principle prediction
Coexistence of ferromagnetism, piezoelectricity and valley in two-dimensional
(2D) materials is crucial to advance multifunctional electronic technologies.
Here, Janus ScXY (XY=Cl, Br and I) monolayers are predicted to be
in-plane piezoelectric ferromagnetic (FM) semiconductors with dynamical,
mechanical and thermal stabilities. The predicted piezoelectric strain
coefficients and (absolute values) are higher than ones of
most 2D materials. Moreover, the (absolute value) of ScClI reaches up
to 1.14 pm/V, which is highly desirable for ultrathin piezoelectric device
application. To obtain spontaneous valley polarization, charge doping are
explored to tune the direction of magnetization of ScXY. By appropriate hole
doping, their easy magnetization axis can change from in-plane to out-of-plane,
resulting in spontaneous valley polarization. Taking ScBrI with 0.20 holes per
f.u. as a example, under the action of an in-plane electric field, the hole
carriers of K valley turn towards one edge of the sample, which will produce
anomalous valley Hall effect (AVHE), and the hole carriers of valley
move in a straight line. These findings could pave the way for designing
piezoelectric and valleytronic devices.Comment: 7 pages,7 figure
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