106 research outputs found
Generalized Nearest Neighbor Decoding
It is well known that for Gaussian channels, a nearest neighbor decoding
rule, which seeks the minimum Euclidean distance between a codeword and the
received channel output vector, is the maximum likelihood solution and hence
capacity-achieving. Nearest neighbor decoding remains a convenient and yet
mismatched solution for general channels, and the key message of this paper is
that the performance of the nearest neighbor decoding can be improved by
generalizing its decoding metric to incorporate channel state dependent output
processing and codeword scaling. Using generalized mutual information, which is
a lower bound to the mismatched capacity under independent and identically
distributed codebook ensemble, as the performance measure, this paper
establishes the optimal generalized nearest neighbor decoding rule, under
Gaussian channel input. Several {restricted forms of the} generalized nearest
neighbor decoding rule are also derived and compared with existing solutions.
The results are illustrated through several case studies for fading channels
with imperfect receiver channel state information and for channels with
quantization effects.Comment: 30 pages, 8 figure
Online media as gatekeepers in the 2016 presidential debate
Professional project report submitted in partial fulfillment of the requirements for the degree of Masters of Arts in Journalism from the School of Journalism, University of Missouri--Columbia.The third Republican presidential debate on CNBC attracted lots of coverage because of controversial performance of both moderators and candidates. I studied the coverage of the debate from the New York Times, USA Today, Politico and Slate, by analyzing 156 stories on the four publications. Under the framework of gatekeeping theory, I found that digital platforms enhanced the diversity of stories' genres, but presentation in digital media is more dramatic, which reinforced gatekeeping bias. Journalists in the digital era had better to find a balance between original reporting and aggregating. Meanwhile, reporters in digital media tend to find more unconventional and interesting angles, while reporters in traditional media preferred more comprehensive stories.Includes bibliographic references
From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective
Nonparametric estimation of the mean and covariance functions is ubiquitous
in functional data analysis and local linear smoothing techniques are most
frequently used. Zhang and Wang (2016) explored different types of asymptotic
properties of the estimation, which reveal interesting phase transition
phenomena based on the relative order of the average sampling frequency per
subject to the number of subjects , partitioning the data into three
categories: ``sparse'', ``semi-dense'' and ``ultra-dense''. In an increasingly
available high-dimensional scenario, where the number of functional variables
is large in relation to , we revisit this open problem from a
non-asymptotic perspective by deriving comprehensive concentration inequalities
for the local linear smoothers. Besides being of interest by themselves, our
non-asymptotic results lead to elementwise maximum rates of convergence
and uniform convergence serving as a fundamentally important tool for further
convergence analysis when grows exponentially with and possibly .
With the presence of extra terms to account for the high-dimensional
effect, we then investigate the scaled phase transitions and the corresponding
elementwise maximum rates from sparse to semi-dense to ultra-dense functional
data in high dimensions. Finally, numerical studies are carried out to confirm
our established theoretical properties
Terahertz Wave Guiding by Femtosecond Laser Filament in Air
Femtosecond laser filament generates strong terahertz (THz) pulse in air. In
this paper, THz pulse waveform generated by femtosecond laser filament has been
experimentally investigated as a function of the length of the filament.
Superluminal propagation of THz pulse has been uncovered, indicating that the
filament creates a THz waveguide in air. Numerical simulation has confirmed
that the waveguide is formed because of the radially non-uniform refractive
index distribution inside the filament. The underlying physical mechanisms and
the control techniques of this type THz pulse generation method might be
revisited based on our findings. It might also potentially open a new approach
for long-distance propagation of THz wave in air.Comment: 5 pages, 6 figure
Distributed CSMA/CA MAC Protocol for RIS-Assisted Networks
This paper focuses on achieving optimal multi-user channel access in
distributed networks using a reconfigurable intelligent surface (RIS). The
network includes wireless channels with direct links between users and RIS
links connecting users to the RIS. To maximize average system throughput, an
optimal channel access strategy is proposed, considering the trade-off between
exploiting spatial diversity gain with RIS assistance and the overhead of
channel probing. The paper proposes an optimal distributed Carrier Sense
Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic
RIS assistance, based on statistics theory of optimal sequential observation
planned decision. Each source-destination pair makes decisions regarding the
use of direct links and/or probing source-RIS-destination links. Channel access
occurs in a distributed manner after successful channel contention. The
optimality of the strategy is rigorously derived using multiple-level pure
thresholds. A distributed algorithm, which achieves significantly lower online
complexity at , is developed to implement the proposed strategy.
Numerical simulations verify the theoretical results and demonstrate the
superior performance compared to existing approaches.Comment: 6 pages, 3 figures, IEEE Global Communications Conference (GLOBECOM)
202
Dynamic Cooperative MAC Optimization in RSU-Enhanced VANETs: A Distributed Approach
This paper presents an optimization approach for cooperative Medium Access
Control (MAC) techniques in Vehicular Ad Hoc Networks (VANETs) equipped with
Roadside Unit (RSU) to enhance network throughput. Our method employs a
distributed cooperative MAC scheme based on Carrier Sense Multiple Access with
Collision Avoidance (CSMA/CA) protocol, featuring selective RSU probing and
adaptive transmission. It utilizes a dual timescale channel access framework,
with a ``large-scale'' phase accounting for gradual changes in vehicle
locations and a ``small-scale'' phase adapting to rapid channel fluctuations.
We propose the RSU Probing and Cooperative Access (RPCA) strategy, a two-stage
approach based on dynamic inter-vehicle distances from the RSU. Using optimal
sequential planned decision theory, we rigorously prove its optimality in
maximizing average system throughput per large-scale phase. For practical
implementation in VANETs, we develop a distributed MAC algorithm with periodic
location updates. It adjusts thresholds based on inter-vehicle and vehicle-RSU
distances during the large-scale phase and accesses channels following the RPCA
strategy with updated thresholds during the small-scale phase. Simulation
results confirm the effectiveness and efficiency of our algorithm.Comment: 6 pages, 5 figures, IEEE ICC 202
I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
With the development of Deep Neural Networks (DNNs), many efforts have been
made to handle medical image segmentation. Traditional methods such as nnUNet
train specific segmentation models on the individual datasets. Plenty of recent
methods have been proposed to adapt the foundational Segment Anything Model
(SAM) to medical image segmentation. However, they still focus on discrete
representations to generate pixel-wise predictions, which are spatially
inflexible and scale poorly to higher resolution. In contrast, implicit methods
learn continuous representations for segmentation, which is crucial for medical
image segmentation. In this paper, we propose I-MedSAM, which leverages the
benefits of both continuous representations and SAM, to obtain better
cross-domain ability and accurate boundary delineation. Since medical image
segmentation needs to predict detailed segmentation boundaries, we designed a
novel adapter to enhance the SAM features with high-frequency information
during Parameter Efficient Fine Tuning (PEFT). To convert the SAM features and
coordinates into continuous segmentation output, we utilize Implicit Neural
Representation (INR) to learn an implicit segmentation decoder. We also propose
an uncertainty-guided sampling strategy for efficient learning of INR.
Extensive evaluations on 2D medical image segmentation tasks have shown that
our proposed method with only 1.6M trainable parameters outperforms existing
methods including discrete and continuous methods. The code will be released
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