437 research outputs found
Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection
Aiming at the problem that the current video anomaly detection cannot fully
use the temporal information and ignore the diversity of normal behavior, an
anomaly detection method is proposed to integrate the spatiotemporal
information of pedestrians. Based on the convolutional autoencoder, the input
frame is compressed and restored through the encoder and decoder. Anomaly
detection is realized according to the difference between the output frame and
the true value. In order to strengthen the characteristic information
connection between continuous video frames, the residual temporal shift module
and the residual channel attention module are introduced to improve the
modeling ability of the network on temporal information and channel
information, respectively. Due to the excessive generalization of convolutional
neural networks, in the memory enhancement modules, the hopping connections of
each codec layer are added to limit autoencoders' ability to represent abnormal
frames too vigorously and improve the anomaly detection accuracy of the
network. In addition, the objective function is modified by a feature
discretization loss, which effectively distinguishes different normal behavior
patterns. The experimental results on the CUHK Avenue and ShanghaiTech datasets
show that the proposed method is superior to the current mainstream video
anomaly detection methods while meeting the real-time requirements.Comment: International Conference on Intelligent Media, Big Data and Knowledge
Minin
Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-antenna Dense Small Cell Networks
This paper studies the performance of cache-enabled dense small cell networks
consisting of multi-antenna sub-6 GHz and millimeter-wave base stations.
Different from the existing works which only consider a single antenna at each
base station, the optimal content placement is unknown when the base stations
have multiple antennas. We first derive the successful content delivery
probability by accounting for the key channel features at sub-6 GHz and mmWave
frequencies. The maximization of the successful content delivery probability is
a challenging problem. To tackle it, we first propose a constrained
cross-entropy algorithm which achieves the near-optimal solution with moderate
complexity. We then develop another simple yet effective heuristic
probabilistic content placement scheme, termed two-stair algorithm, which
strikes a balance between caching the most popular contents and achieving
content diversity. Numerical results demonstrate the superior performance of
the constrained cross-entropy method and that the two-stair algorithm yields
significantly better performance than only caching the most popular contents.
The comparisons between the sub-6 GHz and mmWave systems reveal an interesting
tradeoff between caching capacity and density for the mmWave system to achieve
similar performance as the sub-6 GHz system.Comment: 14 pages; Accepted to appear in IEEE Transactions on Wireless
Communication
Data Augmentation Vision Transformer for Fine-grained Image Classification
Recently, the vision transformer (ViT) has made breakthroughs in image
recognition. Its self-attention mechanism (MSA) can extract discriminative
labeling information of different pixel blocks to improve image classification
accuracy. However, the classification marks in their deep layers tend to ignore
local features between layers. In addition, the embedding layer will be
fixed-size pixel blocks. Input network Inevitably introduces additional image
noise. To this end, we study a data augmentation vision transformer (DAVT)
based on data augmentation and proposes a data augmentation method for
attention cropping, which uses attention weights as the guide to crop images
and improve the ability of the network to learn critical features. Secondly, we
also propose a hierarchical attention selection (HAS) method, which improves
the ability of discriminative markers between levels of learning by filtering
and fusing labels between levels. Experimental results show that the accuracy
of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is
better than the existing mainstream methods, and its accuracy is 1.4\% and
1.6\% higher than the original ViT, respectivelyComment: IEEE Signal Processing Letter
The Verification of Rail Thermal Stress Measurement System
Continuous Welded Rail (CWR) is widely used in modern railways. With the absence of the expansion joints, CWR cannot expansion freely when the temperature changes, which could cause buckling in hot weather or breakage in cold weather. Therefore, rail thermal stress measuring system plays an important role in the safe operation of railways. This paper designed a thermal stress measurement system based on the acoustoelastic effect of the ultrasonic guided wave. A large-scale rail testbed was built to simulate the thermal stress in the rail track, and to establish the relationship of time-delay of guided wave and thermal stress. After laboratory testing, the system was installed in several railway lines in China for field tests. The results showed that the system was stable and accurate in stress measurement. The performance and potentials of the system were discussed
Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control
In recent years, the exponential proliferation of smart devices with their
intelligent applications poses severe challenges on conventional cellular
networks. Such challenges can be potentially overcome by integrating
communication, computing, caching, and control (i4C) technologies. In this
survey, we first give a snapshot of different aspects of the i4C, comprising
background, motivation, leading technological enablers, potential applications,
and use cases. Next, we describe different models of communication, computing,
caching, and control (4C) to lay the foundation of the integration approach. We
review current state-of-the-art research efforts related to the i4C, focusing
on recent trends of both conventional and artificial intelligence (AI)-based
integration approaches. We also highlight the need for intelligence in
resources integration. Then, we discuss integration of sensing and
communication (ISAC) and classify the integration approaches into various
classes. Finally, we propose open challenges and present future research
directions for beyond 5G networks, such as 6G.Comment: This article has been accepted for inclusion in a future issue of
China Communications Journal in IEEE Xplor
A Lightweight Reconstruction Network for Surface Defect Inspection
Currently, most deep learning methods cannot solve the problem of scarcity of
industrial product defect samples and significant differences in
characteristics. This paper proposes an unsupervised defect detection algorithm
based on a reconstruction network, which is realized using only a large number
of easily obtained defect-free sample data. The network includes two parts:
image reconstruction and surface defect area detection. The reconstruction
network is designed through a fully convolutional autoencoder with a
lightweight structure. Only a small number of normal samples are used for
training so that the reconstruction network can be A defect-free reconstructed
image is generated. A function combining structural loss and loss
is proposed as the loss function of the reconstruction network to solve the
problem of poor detection of irregular texture surface defects. Further, the
residual of the reconstructed image and the image to be tested is used as the
possible region of the defect, and conventional image operations can realize
the location of the fault. The unsupervised defect detection algorithm of the
proposed reconstruction network is used on multiple defect image sample sets.
Compared with other similar algorithms, the results show that the unsupervised
defect detection algorithm of the reconstructed network has strong robustness
and accuracy.Comment: Journal of Mathematical Imaging and Vision(JMIV
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