6,933 research outputs found
Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging
We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We show that this illumination coding scheme is highly scalable in achieving flexible resolution, and robust to experimental variations. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5X resolution enhancement across 4X FOVs using only five multiplexed measurements -- more than 10X data reduction over the state-of-the-art. Typical DL algorithms tend to provide over-confident predictions, whose errors are only discovered in hindsight. We develop an uncertainty learning framework to overcome this limitation and provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and "out-of-distribution" testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable AI-augmented large-SBP phase imaging with dependable predictions.Published versio
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
Predictive coding: A Possible Explanation of Filling-in at the blind spot
Filling-in at the blind-spot is a perceptual phenomenon in which the visual
system fills the informational void, which arises due to the absence of retinal
input corresponding to the optic disc, with surrounding visual attributes.
Though there are enough evidence to conclude that some kind of neural
computation is involved in filling-in at the blind spot especially in the early
visual cortex, the knowledge of the actual computational mechanism is far from
complete. We have investigated the bar experiments and the associated
filling-in phenomenon in the light of the hierarchical predictive coding
framework, where the blind-spot was represented by the absence of early
feed-forward connection. We recorded the responses of predictive estimator
neurons at the blind-spot region in the V1 area of our three level (LGN-V1-V2)
model network. These responses are in agreement with the results of earlier
physiological studies and using the generative model we also showed that these
response profiles indeed represent the filling-in completion. These demonstrate
that predictive coding framework could account for the filling-in phenomena
observed in several psychophysical and physiological experiments involving bar
stimuli. These results suggest that the filling-in could naturally arise from
the computational principle of hierarchical predictive coding (HPC) of natural
images.Comment: 23 pages, 9 figure
60 GHz MAC Standardization: Progress and Way Forward
Communication at mmWave frequencies has been the focus in the recent years.
In this paper, we discuss standardization efforts in 60 GHz short range
communication and the progress therein. We compare the available standards in
terms of network architecture, medium access control mechanisms, physical layer
techniques and several other features. Comparative analysis indicates that IEEE
802.11ad is likely to lead the short-range indoor communication at 60 GHz. We
bring to the fore resolved and unresolved issues pertaining to robust WLAN
connectivity at 60 GHz. Further, we discuss the role of mmWave bands in 5G
communication scenarios and highlight the further efforts required in terms of
research and standardization
- …