964 research outputs found
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
Channel-adaptive wireless image transmission with OFDM
We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our approach is adaptive to channel-gain and noise-power variations by exploiting the estimated channel state information (CSI). Specifically, with the proposed dual-attention mechanism, our model can learn to map the features and allocate transmission-power resources judiciously to the available subchannels based on the estimated CSI. Extensive numerical experiments verify that CA-JSCC achieves state-of-the-art performance among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel conditions and can better exploit the limited channel resources by transmitting critical features over better subchannels
A SIMPLE STOCHASTIC MODEL FOR THE SARS-COV-2 EPIDEMIC CURVE
An epidemic curve is a graphic depiction of the number of outbreak cases by date of illness onset, ordinarily constructed after the disease outbreak is over. However, a good estimate of the epidemic curve early in an outbreak would be invaluable to health care officials. On the other hand, from the end of February, the SARS-CoV-2 epidemic in Brazil seems to not following the Europe, or in particular, Italy or Spain. Even if less tests have been applied, there are less deaths occurring in Brazil than in both cited countries. However, due to the few applied tests, there is no certain planning on the real number of active cases. To estimate the number of future cases, epidemiologists make an educated guess as to how many people might become affected. We have proposed a simple fitting model using a simulated annealing technique, testing it with the South Korea data. We have tested and discussed the uncertainties of the model. We also have analyzed the trends in the confirmed cases using this model for the five most affected countries plus Brazil along several epidemic weeks
Consistent Video Filtering for Camera Arrays
International audienceVisual formats have advanced beyond single-view images and videos: 3D movies are commonplace, researchers have developed multi-view navigation systems, and VR is helping to push light field cameras to mass market. However, editing tools for these media are still nascent, and even simple filtering operations like color correction or stylization are problematic: naively applying image filters per frame or per view rarely produces satisfying results due to time and space inconsistencies. Our method preserves and stabilizes filter effects while being agnostic to the inner working of the filter. It captures filter effects in the gradient domain, then uses \emph{input} frame gradients as a reference to impose temporal and spatial consistency. Our least-squares formulation adds minimal overhead compared to naive data processing. Further, when filter cost is high, we introduce a filter transfer strategy that reduces the number of per-frame filtering computations by an order of magnitude, with only a small reduction in visual quality. We demonstrate our algorithm on several camera array formats including stereo videos, light fields, and wide baselines
AirSync: Enabling Distributed Multiuser MIMO with Full Spatial Multiplexing
The enormous success of advanced wireless devices is pushing the demand for
higher wireless data rates. Denser spectrum reuse through the deployment of
more access points per square mile has the potential to successfully meet the
increasing demand for more bandwidth. In theory, the best approach to density
increase is via distributed multiuser MIMO, where several access points are
connected to a central server and operate as a large distributed multi-antenna
access point, ensuring that all transmitted signal power serves the purpose of
data transmission, rather than creating "interference." In practice, while
enterprise networks offer a natural setup in which distributed MIMO might be
possible, there are serious implementation difficulties, the primary one being
the need to eliminate phase and timing offsets between the jointly coordinated
access points.
In this paper we propose AirSync, a novel scheme which provides not only time
but also phase synchronization, thus enabling distributed MIMO with full
spatial multiplexing gains. AirSync locks the phase of all access points using
a common reference broadcasted over the air in conjunction with a Kalman filter
which closely tracks the phase drift. We have implemented AirSync as a digital
circuit in the FPGA of the WARP radio platform. Our experimental testbed,
comprised of two access points and two clients, shows that AirSync is able to
achieve phase synchronization within a few degrees, and allows the system to
nearly achieve the theoretical optimal multiplexing gain. We also discuss MAC
and higher layer aspects of a practical deployment. To the best of our
knowledge, AirSync offers the first ever realization of the full multiuser MIMO
gain, namely the ability to increase the number of wireless clients linearly
with the number of jointly coordinated access points, without reducing the per
client rate.Comment: Submitted to Transactions on Networkin
Compressive Sensing for Spread Spectrum Receivers
With the advent of ubiquitous computing there are two design parameters of
wireless communication devices that become very important power: efficiency and
production cost. Compressive sensing enables the receiver in such devices to
sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in
the two design parameters. This paper investigates the use of Compressive
Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We
show that when using spread spectrum codes in the signal domain, the CS
measurement matrix may be simplified. This measurement scheme, named
Compressive Spread Spectrum (CSS), allows for a simple, effective receiver
design. Furthermore, we numerically evaluate the proposed receiver in terms of
bit error rate under different signal to noise ratio conditions and compare it
with other receiver structures. These numerical experiments show that though
the bit error rate performance is degraded by the subsampling in the CS-enabled
receivers, this may be remedied by including quantization in the receiver
model. We also study the computational complexity of the proposed receiver
design under different sparsity and measurement ratios. Our work shows that it
is possible to subsample a CDMA signal using CSS and that in one example the
CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE
Transactions on Wireless Communication
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