164 research outputs found
Approaching quantum anomalous Hall effect in proximity-coupled YIG/graphene/h-BN sandwich structure
Quantum anomalous Hall state is expected to emerge in Dirac electron systems
such as graphene under both sufficiently strong exchange and spin-orbit
interactions. In pristine graphene, neither interaction exists; however, both
interactions can be acquired by coupling graphene to a magnetic insulator (MI)
as revealed by the anomalous Hall effect. Here, we show enhanced magnetic
proximity coupling by sandwiching graphene between a ferrimagnetic insulator
yttrium iron garnet (YIG) and hexagonal-boron nitride (h-BN) which also serves
as a top gate dielectric. By sweeping the top-gate voltage, we observe Fermi
level-dependent anomalous Hall conductance. As the Dirac point is approached
from both electron and hole sides, the anomalous Hall conductance reaches 1/4
of the quantum anomalous Hall conductance 2e2/h. The exchange coupling strength
is determined to be as high as 27 meV from the transition temperature of the
induced magnetic phase. YIG/graphene/h-BN is an excellent heterostructure for
demonstrating proximity-induced interactions in two-dimensional electron
systems
Sparse Complementary Pairs with Additional Aperiodic ZCZ Property
This paper presents a novel class of complex-valued sparse complementary
pairs (SCPs), each consisting of a number of zero values and with additional
zero-correlation zone (ZCZ) property for the aperiodic autocorrelations and
crosscorrelations of the two constituent sequences. Direct constructions of
SCPs and their mutually-orthogonal mates based on restricted generalized
Boolean functions are proposed. It is shown that such SCPs exist with arbitrary
lengths and controllable sparsity levels, making them a disruptive sequence
candidate for modern low-complexity, low-latency, and low-storage signal
processing applications
Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving
In the context of autonomous driving, the iterative linear quadratic
regulator (iLQR) is known to be an efficient approach to deal with the
nonlinear vehicle models in motion planning problems. Particularly, the
constrained iLQR algorithm has shown noteworthy advantageous outcomes of
computation efficiency in achieving motion planning tasks under general
constraints of different types. However, the constrained iLQR methodology
requires a feasible trajectory at the first iteration as a prerequisite. Also,
the methodology leaves open the possibility for incorporation of fast,
efficient, and effective optimization methods (i.e., fast-solvers) to further
speed up the optimization process such that the requirements of real-time
implementation can be successfully fulfilled. In this paper, a well-defined and
commonly-encountered motion planning problem is formulated under nonlinear
vehicle dynamics and various constraints, and an alternating direction method
of multipliers (ADMM) is developed to determine the optimal control actions.
With this development, the approach is able to circumvent the feasibility
requirement of the trajectory at the first iteration. An illustrative example
of motion planning in autonomous vehicles is then investigated with different
driving scenarios taken into consideration. As clearly observed from the
simulation results, the significance of this work in terms of obstacle
avoidance is demonstrated. Furthermore, a noteworthy achievement of high
computation efficiency is attained; and as a result, real-time computation and
implementation can be realized through this framework, and thus it provides
additional safety to the on-road driving tasks.Comment: 9 pages, 8 figure
Neural Network iLQR: A New Reinforcement Learning Architecture
As a notable machine learning paradigm, the research efforts in the context
of reinforcement learning have certainly progressed leaps and bounds. When
compared with reinforcement learning methods with the given system model, the
methodology of the reinforcement learning architecture based on the unknown
model generally exhibits significantly broader universality and applicability.
In this work, a new reinforcement learning architecture is developed and
presented without the requirement of any prior knowledge of the system model,
which is termed as an approach of a "neural network iterative linear quadratic
regulator (NNiLQR)". Depending solely on measurement data, this method yields a
completely new non-parametric routine for the establishment of the optimal
policy (without the necessity of system modeling) through iterative refinements
of the neural network system. Rather importantly, this approach significantly
outperforms the classical iterative linear quadratic regulator (iLQR) method in
terms of the given objective function because of the innovative utilization of
further exploration in the methodology. As clearly indicated from the results
attained in two illustrative examples, these significant merits of the NNiLQR
method are demonstrated rather evidently.Comment: 13 pages, 9 figure
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