724 research outputs found
Source and Physical-Layer Network Coding for Correlated Two-Way Relaying
In this paper, we study a half-duplex two-way relay channel (TWRC) with
correlated sources exchanging bidirectional information. In the case, when both
sources have the knowledge of correlation statistics, a source compression with
physical-layer network coding (SCPNC) scheme is proposed to perform the
distributed compression at each source node. When only the relay has the
knowledge of correlation statistics, we propose a relay compression with
physical-layer network coding (RCPNC) scheme to compress the bidirectional
messages at the relay. The closed-form block error rate (BLER) expressions of
both schemes are derived and verified through simulations. It is shown that the
proposed schemes achieve considerable improvements in both error performance
and throughput compared with the conventional non-compression scheme in
correlated two-way relay networks (CTWRNs).Comment: 15 pages, 6 figures. IET Communications, 201
Selective Combining for Hybrid Cooperative Networks
In this study, we consider the selective combining in hybrid cooperative
networks (SCHCNs scheme) with one source node, one destination node and
relay nodes. In the SCHCN scheme, each relay first adaptively chooses between
amplify-and-forward protocol and decode-and-forward protocol on a per frame
basis by examining the error-detecting code result, and () relays will be selected to forward their received signals to the
destination. We first develop a signal-to-noise ratio (SNR) threshold-based
frame error rate (FER) approximation model. Then, the theoretical FER
expressions for the SCHCN scheme are derived by utilizing the proposed SNR
threshold-based FER approximation model. The analytical FER expressions are
validated through simulation results.Comment: 27 pages, 8 figures, IET Communications, 201
A Bayesian predictive classification approach to robust speech recognition
We introduce a new decision strategy called Bayesian predictive classification (BPC) for robust speech recognition where an unknown mismatch between the training and testing conditions exists. We then propose and focus on one of the approximate BPC approaches called quasi-Bayes predictive classification (QBPC). In a series of comparative experiments where the mismatch is caused by additive white Gaussian noise, we show that the proposed QBPC approach achieves a considerable improvement over the conventional plug-in MAP decision rule.published_or_final_versio
Study on Downlink Spectral Efficiency in Orthogonal Frequency Division Multiple Access Systems
In previous studies on the capacity of orthogonal frequency division multiple
access (OFDMA) systems, it is usually assumed that co-channel interference
(CCI) from adjacent cells is a Gaussian-distributed random variable. However,
very-little work shows that the Gaussian assumption does not hold true in OFDMA
systems. In this paper, the statistical property of CCI in downlink OFDMA
systems is studied, and spectral efficiency of downlink OFDMA system is
analyzed based on the derived statistical model. First, the probability density
function (PDF) of CCI in downlink OFDMA cellular systems is studied with the
considerations of path loss, multipath fading and Gaussian-like transmit
signals. Moreover, some closed-form expressions of the PDF are obtained for
special cases. The derived results show that the PDFs of CCI are with a heavy
tail, and significantly deviate from the Gaussian distribution. Then, based on
the derived statistical properties of CCI, the downlink spectral efficiency is
derived. Numerical and simulation results justify the derived statistical CCI
model and spectral efficiency.Comment: 23 pages, 8 figures, IET Communications, 201
Robust Table Detection and Structure Recognition from Heterogeneous Document Images
We introduce a new table detection and structure recognition approach named
RobusTabNet to detect the boundaries of tables and reconstruct the cellular
structure of each table from heterogeneous document images. For table
detection, we propose to use CornerNet as a new region proposal network to
generate higher quality table proposals for Faster R-CNN, which has
significantly improved the localization accuracy of Faster R-CNN for table
detection. Consequently, our table detection approach achieves state-of-the-art
performance on three public table detection benchmarks, namely cTDaR TrackA,
PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone
network. Furthermore, we propose a new split-and-merge based table structure
recognition approach, in which a novel spatial CNN based separation line
prediction module is proposed to split each detected table into a grid of
cells, and a Grid CNN based cell merging module is applied to recover the
spanning cells. As the spatial CNN module can effectively propagate contextual
information across the whole table image, our table structure recognizer can
robustly recognize tables with large blank spaces and geometrically distorted
(even curved) tables. Thanks to these two techniques, our table structure
recognition approach achieves state-of-the-art performance on three public
benchmarks, including SciTSR, PubTabNet and cTDaR TrackB2-Modern. Moreover, we
have further demonstrated the advantages of our approach in recognizing tables
with complex structures, large blank spaces, as well as geometrically distorted
or even curved shapes on a more challenging in-house dataset.Comment: Accepted by Pattern Recognition on 27 Aug. 202
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