274 research outputs found
Utility greedy discrete bit loading for interference limited multi-cell OFDM system
In this contribution we present the solution of the utility greedy discrete bit loading for interference limited multicell OFDM networks. Setting the utility as the sum of consumed power proportions, the algorithm follows greedy way to achieve the maximum throughput of the system. Simulation has shown that the proposed algorithm has better performance and lower complexity than the traditional optimal algorithm. The discussion of the results is provided
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
Fine-grained visual recognition aims to capture discriminative
characteristics amongst visually similar categories. The state-of-the-art
research work has significantly improved the fine-grained recognition
performance by deep metric learning using triplet network. However, the impact
of intra-category variance on the performance of recognition and robust feature
representation has not been well studied. In this paper, we propose to leverage
intra-class variance in metric learning of triplet network to improve the
performance of fine-grained recognition. Through partitioning training images
within each category into a few groups, we form the triplet samples across
different categories as well as different groups, which is called Group
Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is
strengthened by incorporating intra-class variance with GS-TRS, which may
contribute to the optimization objective of triplet network. Extensive
experiments over benchmark datasets CompCar and VehicleID show that the
proposed GS-TRS has significantly outperformed state-of-the-art approaches in
both classification and retrieval tasks.Comment: 6 pages, 5 figure
Research of the wrinkling elimination of stainless steel SUS304 by viscous pressure
Wrinkling is one of the most important factors
influencing a forming precision of sheet metal,
which brings difficulties to the forming process of
sheet metal. In order to eliminate the wrinkling
during the forming process, an accurate prediction
is necessary. In this paper, the wrinkling
elimination process was investigated based on the
principle of the Yoshida Buckling Test (YBT) and
viscous pressure forming. The experimental device
was designed, and evaluation method of the
wrinkling elimination rate was presented by the
stainless steel SUS304. On this basis, the wrinkling
elimination experiment was carried out, the
influences of both the viscous medium molecular
weight and the tensile state of wrinkle under the
viscous pressure on the wrinkling elimination were
obtained
Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios
Self-supervised denoising has attracted widespread attention due to its
ability to train without clean images. However, noise in real-world scenarios
is often spatially correlated, which causes many self-supervised algorithms
based on the pixel-wise independent noise assumption to perform poorly on
real-world images. Recently, asymmetric pixel-shuffle downsampling (AP) has
been proposed to disrupt the spatial correlation of noise. However,
downsampling introduces aliasing effects, and the post-processing to eliminate
these effects can destroy the spatial structure and high-frequency details of
the image, in addition to being time-consuming. In this paper, we
systematically analyze downsampling-based methods and propose an Asymmetric
Tunable Blind-Spot Network (AT-BSN) to address these issues. We design a
blind-spot network with a freely tunable blind-spot size, using a large
blind-spot during training to suppress local spatially correlated noise while
minimizing damage to the global structure, and a small blind-spot during
inference to minimize information loss. Moreover, we propose blind-spot
self-ensemble and distillation of non-blind-spot network to further improve
performance and reduce computational complexity. Experimental results
demonstrate that our method achieves state-of-the-art results while
comprehensively outperforming other self-supervised methods in terms of image
texture maintaining, parameter count, computation cost, and inference time
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