1,183 research outputs found
Message Passing-Based Joint User Activity Detection and Channel Estimation for Temporally-Correlated Massive Access
This paper studies the user activity detection and channel estimation problem
in a temporally-correlated massive access system where a very large number of
users communicate with a base station sporadically and each user once activated
can transmit with a large probability over multiple consecutive frames. We
formulate the problem as a dynamic compressed sensing (DCS) problem to exploit
both the sparsity and the temporal correlation of user activity. By leveraging
the hybrid generalized approximate message passing (HyGAMP) framework, we
design a computationally efficient algorithm, HyGAMP-DCS, to solve this
problem. In contrast to only exploit the historical estimations, the proposed
algorithm performs bidirectional message passing between the neighboring frames
for activity likelihood update to fully exploit the temporally-correlated user
activities. Furthermore, we develop an expectation maximization HyGAMP-DCS
(EM-HyGAMP-DCS) algorithm to adaptively learn the hyperparameters during the
estimation procedure when the system statistics are unknown. In particular, we
propose to utilize the analysis tool of state evolution to find the appropriate
hyperparameter initialization of EM-HyGAMP-DCS. Simulation results demonstrate
that our proposed algorithms can significantly improve the user activity
detection accuracy and reduce the channel estimation error.Comment: 31 pages, 14 figures, minor revisio
An Empirical Study of Technology Diffusion and International Trade in korea: Using patent Application Data
International trade is an important conduit for international technology diffusion.
Considering the endogenous growth theory, a rapid increase of foreign patent application,
and international trade in Korea, it seems meaningful to study the role of international trade
in the technology diffusion from foreign countries to Korea. This paper investigates the
relationship between the trade and technology diffusion by using Korean patent data and
trade data. We found that the international trade of Korea with foreign countries was very
significant variable
Cooperative Multi-Cell Massive Access with Temporally Correlated Activity
This paper investigates the problem of activity detection and channel
estimation in cooperative multi-cell massive access systems with temporally
correlated activity, where all access points (APs) are connected to a central
unit via fronthaul links. We propose to perform user-centric AP cooperation for
computation burden alleviation and introduce a generalized sliding-window
detection strategy for fully exploiting the temporal correlation in activity.
By establishing the probabilistic model associated with the factor graph
representation, we propose a scalable Dynamic Compressed Sensing-based Multiple
Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP)
algorithm from the perspective of Bayesian inference. Therein, the activity
likelihood is refined by performing standard message passing among the
activities in the spatial-temporal domain and GAMP is employed for efficient
channel estimation. Furthermore, we develop two schemes of quantize-and-forward
(QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the
finite-fronthaul-capacity scenario, which are extensively evaluated under
various system limits. Numerical results verify the significant superiority of
the proposed approach over the benchmarks. Moreover, it is revealed that QF can
usually realize superior performance when the antenna number is small, whereas
DF shifts to be preferable with limited fronthaul capacity if the large-scale
antenna arrays are equipped.Comment: 16 pages, 17 figures, minor revisio
Development of an indirect enzyme-linked immunosorbent assay (ELISA) assay based on a recombinant truncated VP2 (tVP2) protein for the detection of canine parvovirus antibodies
By removing the N-terminal hydrophobic sequence, truncated VP2 (tVP2) genes were cloned into the pET-32a (+) plasmid and subsequently expressed as His fusion proteins. The purified recombinant tVP2 proteins were specific to canine parvovirus (CPV), and one of them was used in an indirect enzyme-linked immunosorbent assay (ELISA) for the detection of CPV antibodies. The minimum detection limit of this method was 1:1280. There was good agreement between tVP2-based indirect ELISA and the commercially available diagnostic kit. The results suggest that the recombinant tVP2 protein-based ELISA could be used to detect CPV antibodies.Key words: Canine parvovirus, recombinant truncated VP2 (tVP2), enzyme-linked immunosorbent assay (ELISA), antibody detection
Positive selection on hemagglutinin and neuraminidase genes of H1N1 influenza viruses
BACKGROUND: Since its emergence in March 2009, the pandemic 2009 H1N1 influenza A virus has posed a serious threat to public health. To trace the evolutionary path of these new pathogens, we performed a selection-pressure analysis of a large number of hemagglutinin (HA) and neuraminidase (NA) gene sequences of H1N1 influenza viruses from different hosts.
RESULTS: Phylogenetic analysis revealed that both HA and NA genes have evolved into five distinct clusters, with further analyses indicating that the pandemic 2009 strains have experienced the strongest positive selection. We also found evidence of strong selection acting on the seasonal human H1N1 isolates. However, swine viruses from North America and Eurasia were under weak positive selection, while there was no significant evidence of positive selection acting on the avian isolates. A site-by-site analysis revealed that the positively selected sites were located in both of the cleaved products of HA (HA1 and HA2), as well as NA. In addition, the pandemic 2009 strains were subject to differential selection pressures compared to seasonal human, North American swine and Eurasian swine H1N1 viruses.
CONCLUSIONS: Most of these positively and/or differentially selected sites were situated in the B-cell and/or T-cell antigenic regions, suggesting that selection at these sites might be responsible for the antigenic variation of the viruses. Moreover, some sites were also associated with glycosylation and receptor-binding ability. Thus, selection at these positions might have helped the pandemic 2009 H1N1 viruses to adapt to the new hosts after they were introduced from pigs to humans. Positive selection on position 274 of NA protein, associated with drug resistance, might account for the prevalence of drug-resistant variants of seasonal human H1N1 influenza viruses, but there was no evidence that positive selection was responsible for the spread of the drug resistance of the pandemic H1N1 strains
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Identifying the most influential roads based on traffic correlation networks
Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s)
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