109 research outputs found
Long Short-Term Memory Spatial Transformer Network
Spatial transformer network has been used in a layered form in conjunction
with a convolutional network to enable the model to transform data spatially.
In this paper, we propose a combined spatial transformer network (STN) and a
Long Short-Term Memory network (LSTM) to classify digits in sequences formed by
MINST elements. This LSTM-STN model has a top-down attention mechanism profit
from LSTM layer, so that the STN layer can perform short-term independent
elements for the statement in the process of spatial transformation, thus
avoiding the distortion that may be caused when the entire sequence is
spatially transformed. It also avoids the influence of this distortion on the
subsequent classification process using convolutional neural networks and
achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional
Neural Network with STN layer
An Empirical Study on use of Social Media in the Hotel Industry in China: A Study of Customers’ Preferences and Attitudes
With the development of the technology, business corporations start to use social media to promote their businesses. This study focuses on the preferences and attitudes of travelers’ in China with the relationship between usage of social media (based on WeChat and Sina Weibo) with the option of hotel choice in the hospitality industry. There are two instruments used in this study: (1) sending out online surveys and (2) scheduled interviews with people who are working in the hotel industry. From our survey data (N=245) were participants who completed questionnaires located all over China. However, the results of the study indicate that participating hotels and online tickets firms prefer to use social media to attract guests and even potential consumers. The top about three elements that influence decision making of hotel choice are: price (71.84%); location (68.16%); and online rating (33.06%)
Impact of SZ cluster residuals in CMB maps and CMB-LSS cross-correlations
Residual foreground contamination in cosmic microwave background (CMB) maps,
such as the residual contamination from thermal Sunyaev-Zeldovich (SZ) effect
in the direction of galaxy clusters, can bias the cross-correlation
measurements between CMB and large-scale structure optical surveys. It is thus
essential to quantify those residuals and, if possible, to null out SZ cluster
residuals in CMB maps. We quantify for the first time the amount of SZ cluster
contamination in the released Planck 2015 CMB maps through (i) the stacking of
CMB maps in the direction of the clusters, and (ii) the computation of
cross-correlation power spectra between CMB maps and the SDSS-IV large-scale
structure data. Our cross-power spectrum analysis yields a detection
at the cluster scale () and a detection on larger
scales () due to clustering of SZ clusters, giving an overall
detection of SZ cluster residuals in the Planck CMB maps. The Planck
2015 NILC CMB map is shown to have of thermal SZ foreground emission
left in it. Using the 'Constrained ILC' component separation technique, we
construct an alternative Planck CMB map, the 2D-ILC map, which is shown to have
negligible SZ contamination, at the cost of being slightly more contaminated by
Galactic foregrounds and noise. We also discuss the impact of the SZ residuals
in CMB maps on the measurement of the ISW effect, which is shown to be
negligible based on our analysis.Comment: 14 pages, 11 figures, 1 table, accepted by MNRAS, close to the
published versio
Impact of Simulated 1/f Noise for HI Intensity Mapping Experiments
Cosmology has entered an era where the experimental limitations are not due
to instrumental sensitivity but instead due to inherent systematic
uncertainties in the instrumentation and data analysis methods. The field of HI
intensity mapping (IM) is still maturing, however early attempts are already
systematics limited. One such systematic limitation is 1/f noise, which largely
originates within the instrumentation and manifests as multiplicative gain
fluctuations. To date there has been little discussion about the possible
impact of 1/f noise on upcoming single-dish HI IM experiments such as BINGO,
FAST or SKA. Presented in this work are Monte-Carlo end-to-end simulations of a
30 day HI IM survey using the SKA-MID array covering a bandwidth of 950 and
1410 MHz. These simulations extend 1/f noise models to include not just
temporal fluctuations but also correlated gain fluctuations across the receiver
bandpass. The power spectral density of the spectral gain fluctuations are
modelled as a power-law, and characterised by a parameter . It is found
that the degree of 1/f noise frequency correlation will be critical to the
success of HI IM experiments. Small values of ( < 0.25) or high
correlation is preferred as this is more easily removed using current component
separation techniques. The spectral index of temporal fluctuations ()
is also found to have a large impact on signal-to-noise. Telescope slew speed
has a smaller impact, and a scan speed of 1 deg s should be sufficient
for a HI IM survey with the SKA.Comment: 22 pages, 15 figures, 2 table
MUSE-Fi: Contactless MUti-person SEnsing Exploiting Near-field Wi-Fi Channel Variation
Having been studied for more than a decade, Wi-Fi human sensing still faces a
major challenge in the presence of multiple persons, simply because the limited
bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically
separate multiple subjects. Existing solutions mostly avoid this challenge by
switching to radars with GHz bandwidth, at the cost of cumbersome deployments.
Therefore, could Wi-Fi human sensing handle multiple subjects remains an open
question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing
system with physical separability. The principle behind MUSE-Fi is that, given
a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field
channel variation caused by the subject significantly overwhelms variations
caused by other distant subjects. Consequently, focusing on the channel state
information (CSI) carried by the traffic in and out of this device naturally
allows for physically separating multiple subjects. Based on this principle, we
propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI,
and iii) downlink beamforming feedback, where we specifically tackle signal
recovery from sparse (per-user) traffic under realistic multi-user
communication scenarios. Our extensive evaluations clearly demonstrate that
MUSE-Fi is able to successfully handle multi-person sensing with respect to
three typical applications: respiration monitoring, gesture detection, and
activity recognition.Comment: 15 pages. Accepted by ACM MobiCom 202
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning
Positioning is an essential service for various applications and is expected
to be integrated with existing communication infrastructures in 5G and 6G.
Though current Wi-Fi and cellular base stations (BSs) can be used to support
this integration, the resulting precision is unsatisfactory due to the lack of
precise control of the wireless signals. Recently, BSs adopting reconfigurable
holographic surfaces (RHSs) have been advocated for positioning as RHSs' large
number of antenna elements enable generation of arbitrary and highly-focused
signal beam patterns. However, existing designs face two major challenges: i)
RHSs only have limited operating bandwidth, and ii) the positioning methods
cannot adapt to the diverse environments encountered in practice. To overcome
these challenges, we present HoloFed, a system providing high-precision
environment-adaptive user positioning services by exploiting multi-band(MB)-RHS
and federated learning (FL). For improving the positioning performance, a lower
bound on the error variance is obtained and utilized for guiding MB-RHS's
digital and analog beamforming design. For better adaptability while preserving
privacy, an FL framework is proposed for users to collaboratively train a
position estimator, where we exploit the transfer learning technique to handle
the lack of position labels of the users. Moreover, a scheduling algorithm for
the BS to select which users train the position estimator is designed, jointly
considering the convergence and efficiency of FL. Our simulation results
confirm that HoloFed achieves a 57% lower positioning error variance compared
to a beam-scanning baseline and can effectively adapt to diverse environments
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