Abstract: Communication signal modulation recognition
technology is one of the key technologies in the field of modern
information warfare. At present, communication signal automatic
modulation recognition methods are mainly divided into two major
categories. One is the maximum likelihood hypothesis testing method
based on decision theory, the other is a statistical pattern recognition
method based on feature extraction. Now, the most commonly used
is a statistical pattern recognition method, which includes feature
extraction and classifier design. With the increasingly complex
electromagnetic environment of communications, how to effectively
extract the features of various signals at low signal-to-noise ratio
(SNR) is a hot topic for scholars in various countries. To solve this
problem, this paper proposes a feature extraction algorithm for the
communication signal based on the improved Holder cloud feature.
And the extreme learning machine (ELM) is used which aims at
the problem of the real-time in the modern warfare to classify
the extracted features. The algorithm extracts the digital features
of the improved cloud model without deterministic information in
a low SNR environment, and uses the improved cloud model to
obtain more stable Holder cloud features and the performance of the
algorithm is improved. This algorithm addresses the problem that
a simple feature extraction algorithm based on Holder coefficient
feature is difficult to recognize at low SNR, and it also has a
better recognition accuracy. The results of simulations show that the
approach in this paper still has a good classification result at low
SNR, even when the SNR is -15dB, the recognition accuracy still
reaches 76%.
Abstract: Bovine viral diarrhea virus (BVDV) can cause lifelong
persistent infection. One reason for the phenomena is attributed to
BVDV infection to placenta tissue. However the mechanisms that
BVDV invades into placenta tissue remain unclear. To clarify the
molecular mechanisms, we investigated the possible means that
BVDV entered into bovine trophoblast cells (TPC). Yeast two-hybrid
system was used to identify proteins extracted from TPC, which
interact with BVDV envelope glycoprotein E2. A PGbkt7-E2 yeast
expression vector and TPC cDNA library were constructed. Through
two rounds of screening, three positive clones were identified.
Sequencing analysis indicated that all the three positive clones
encoded the same protein clathrin. Physical interaction between
clathrin and BVDV E2 protein was further confirmed by
coimmunoprecipitation experiments. This result suggested that the
clathrin might play a critical role in the process of BVDV entry into
placenta tissue and might be a novel antiviral target for preventing
BVDV infection.
Abstract: Sparse representation has long been studied and several
dictionary learning methods have been proposed. The dictionary
learning methods are widely used because they are adaptive. In this
paper, a new dictionary learning method for audio is proposed. Signals
are at first decomposed into different degrees of Intrinsic Mode
Functions (IMF) using Empirical Mode Decomposition (EMD)
technique. Then these IMFs form a learned dictionary. To reduce the
size of the dictionary, the K-means method is applied to the dictionary
to generate a K-EMD dictionary. Compared to K-SVD algorithm, the
K-EMD dictionary decomposes audio signals into structured
components, thus the sparsity of the representation is increased by
34.4% and the SNR of the recovered audio signals is increased by
20.9%.
Abstract: One of the difficulties of the vibration-based damage identification methods is the nonuniqueness of the results of damage identification. The different damage locations and severity may cause the identical response signal, which is even more severe for detection of the multiple damage. This paper proposes a new strategy for damage detection to avoid this nonuniqueness. This strategy firstly determines the approximates damage area based on the statistical pattern recognition method using the dynamic strain signal measured by the distributed fiber Bragg grating, and then accurately evaluates the damage information based on the Bayesian model updating method using the experimental modal data. The stochastic simulation method is then used to compute the high-dimensional integral in the Bayesian problem. Finally, an experiment of the plate structure, simulating one part of mechanical structure, is used to verify the effectiveness of this approach.
Abstract: As part of national epidemiological survey on bovine
viral diarrhea virus (BVDV), a total of 274 dejecta samples were
collected from 14 cattle farms in 8 areas of Xinjiang Uygur
Autonomous Region in northwestern China. Total RNA was extracted
from each sample, and 5--untranslated region (UTR) of BVDV
genome was amplified by using two-step reverse
transcriptase-polymerase chain reaction (RT-PCR). The PCR products
were subsequently sequenced to study the genetic variations of BVDV
in these areas. Among the 274 samples, 33 samples were found
virus-positive. According to sequence analysis of the PCR products,
the 33 samples could be arranged into 16 groups. All the sequences,
however, were highly conserved with BVDV Osloss strains. The virus
possessed theses sequences belonged to BVDV-1b subtype by
phylogenetic analysis. Based on these data, we established a typing
tree for BVDV in these areas. Our results suggested that BVDV-1b
was a predominant subgenotype in northwestern China and no
correlation between the genetic and geographical distances could be
observed above the farm level.