3,490 research outputs found
Magnetoresistance from Fermi Surface Topology
Extremely large non-saturating magnetoresistance has recently been reported
for a large number of both topologically trivial and non-trivial materials.
Different mechanisms have been proposed to explain the observed
magnetotransport properties, yet without arriving to definitive conclusions or
portraying a global picture. In this work, we investigate the transverse
magnetoresistance of materials by combining the Fermi surfaces calculated from
first principles with the Boltzmann transport theory approach relying on the
semiclassical model and the relaxation time approximation. We first consider a
series of simple model Fermi surfaces to provide a didactic introduction into
the charge-carrier compensation and open-orbit mechanisms leading to
non-saturating magnetoresistance. We then address in detail magnetotransport in
three representative materials: (i) copper, a prototypical nearly free-electron
metal characterized by the open Fermi surface that results in an intricate
angular magnetoresistance, (ii) bismuth, a topologically trivial semimetal in
which very large magnetoresistance is known to result from charge-carrier
compensation, and (iii) tungsten diphosphide WP2, a recently discovered type-II
Weyl semimetal that holds the record of magnetoresistance in compounds. In all
three cases our calculations show excellent agreement with both the field
dependence of magnetoresistance and its anisotropy measured at low
temperatures. Furthermore, the calculations allow for a full interpretation of
the observed features in terms of the Fermi surface topology. These results
will help addressing a number of outstanding questions, such as the role of the
topological phase in the pronounced large non-saturating magnetoresistance
observed in topological materials.Comment: 13 pages, 9 figure
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
An electroencephalography (EEG) based brain activity recognition is a
fundamental field of study for a number of significant applications such as
intention prediction, appliance control, and neurological disease diagnosis in
smart home and smart healthcare domains. Existing techniques mostly focus on
binary brain activity recognition for a single person, which limits their
deployment in wider and complex practical scenarios. Therefore, multi-person
and multi-class brain activity recognition has obtained popularity recently.
Another challenge faced by brain activity recognition is the low recognition
accuracy due to the massive noises and the low signal-to-noise ratio in EEG
signals. Moreover, the feature engineering in EEG processing is time-consuming
and highly re- lies on the expert experience. In this paper, we attempt to
solve the above challenges by proposing an approach which has better EEG
interpretation ability via raw Electroencephalography (EEG) signal analysis for
multi-person and multi-class brain activity recognition. Specifically, we
analyze inter-class and inter-person EEG signal characteristics, based on which
to capture the discrepancy of inter-class EEG data. Then, we adopt an
Autoencoder layer to automatically refine the raw EEG signals by eliminating
various artifacts. We evaluate our approach on both a public and a local EEG
datasets and conduct extensive experiments to explore the effect of several
factors (such as normalization methods, training data size, and Autoencoder
hidden neuron size) on the recognition results. The experimental results show
that our approach achieves a high accuracy comparing to competitive
state-of-the-art methods, indicating its potential in promoting future research
on multi-person EEG recognition.Comment: 10 page
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables
people to communicate with the outside world by interpreting the EEG signals of
their brains to interact with devices such as wheelchairs and intelligent
robots. More specifically, motor imagery EEG (MI-EEG), which reflects a
subjects active intent, is attracting increasing attention for a variety of BCI
applications. Accurate classification of MI-EEG signals while essential for
effective operation of BCI systems, is challenging due to the significant noise
inherent in the signals and the lack of informative correlation between the
signals and brain activities. In this paper, we propose a novel deep neural
network based learning framework that affords perceptive insights into the
relationship between the MI-EEG data and brain activities. We design a joint
convolutional recurrent neural network that simultaneously learns robust
high-level feature presentations through low-dimensional dense embeddings from
raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various
artifacts such as background activities. The proposed approach has been
evaluated extensively on a large- scale public MI-EEG dataset and a limited but
easy-to-deploy dataset collected in our lab. The results show that our approach
outperforms a series of baselines and the competitive state-of-the- art
methods, yielding a classification accuracy of 95.53%. The applicability of our
proposed approach is further demonstrated with a practical BCI system for
typing.Comment: 10 page
Semiquantum key distribution using entangled states
Recently, Boyer et al. presented a novel semiquantum key distribution
protocol [M. Boyer, D. Kenigsberg, and T. Mor, Phys. Rev. Lett. 99, 140501
(2007)], by using four quantum states, each of which is randomly prepared by Z
basis or X basis. Here we present a semiquantum key distribution protocol by
using entangled states in which quantum Alice shares a secret key with
classical Bob. We also show the protocol is secure against eavesdropping.Comment: 6 page
Giant vortex and Skyrmion in a rotating two-species Bose-Einstein condensate
Numerical simulations are performed for a rotating two-species Bose
condensate confined by a harmonic potential. The particle numbers of each
species are unequal. When the rotational speed exceeds a critical value, the
majority species reside in the center of the potential while the minority
species is pushed out to the outskirts, forming a giant vortex hole to contain
the majority species. A novel annular Skyrmion forms at the interface of the
two species.Comment: 5 pages, 5 figure
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