Mr. Xuehong Sun Profile

Xuehong Sun

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 January 2025 Open Access

A flaring planar dipole array antenna

Tingting Zhang, Xuehong Sun

Proceedings Volume 13513, 135132H (2025) https://doi.org/10.1117/12.3045719

KEYWORDS: Antennas, Dielectrics, Design, Extremely high frequency, Electromagnetism, Radar, Deformation, Wireless communications, Microwave radiation, Metals

Read Abstract +

In this paper, based on the horn dipole antenna unit, the integrated array antenna can meet the requirements of high gain, high efficiency and high integration of millimeter wave and wireless communication. The antenna consists of five metal dipole elements arranged equidistant, and two opposite symmetrical segments with dielectric plates as dipoles are fed between metal plates. Dielectric plate is made of Teflon, a high frequency composite material, which is widely used in high frequency signal transmission and microwave communication, and its dielectric constant is very low. Achieve below -10dB |S11| in the frequency band 1.352GHz-2.274GHz, with a maximum gain of 18.6dB. Therefore, the antenna is suitable for millimeter wave communication.

DOWNLOAD PAPER

Proceedings Article | 11 October 2023 Paper

Research on improving TransUnet network for feature classification

Jiangjie Yin, Ci Wang, LiPing Liu, Haolong Yang, Xuehong Sun, Yanni Wang

Proceedings Volume 12800, 128003D (2023) https://doi.org/10.1117/12.3004083

KEYWORDS: Image segmentation, Convolution, Education and training, Remote sensing, Image classification, Vegetation, Buildings, Semantics, Network architectures, Matrices

Read Abstract +

To address the low accuracy of TransUent network in small-area feature recognition, two feature classification networks based on improved TransUent network are proposed: DS-TransUnet network and DS-A-TransUnet network.DS-TransUnet network introduces a residual module in the bottom layer of the Convolutional Neural Network (CNN) module of the hybrid encoder of TransUent network to deepen the network and uses convolutional operation instead of maximum pooling in the hybrid encoder to reduce the loss of feature information. The DS-TransUnet network introduces a residual module at the bottom of the Convolutional Neural Networks (CNN) module of the TransUent network to deepen the network, and uses convolutional operations instead of maximum pooling in the hybrid encoder to reduce the loss of feature information, and uses depth-separable convolution instead of regular convolution to reduce the residual blocks and the large number of parameters caused by convolutional operations; the DS-A-TransUnet network On the basis of DS-TransUnet network, a void space pyramidal pooling module is introduced, and the classification accuracy is further improved by using void convolution with different expansion rates in parallel to obtain different size of perceptual fields and fully extract multi-scale features. The experimental results show that the recognition accuracy of DS-TransUnet network is 3.06% and 2.85% higher than that of TransUent network for arable land and water bodies, respectively, and the overall recognition accuracy reaches 89.14%; the recognition accuracy of DS-A-TransUnet network is 3.17% and 4.79% higher than that of TransUent network for arable land and water bodies, respectively, and the overall recognition accuracy reaches 90.53%. The overall recognition accuracy reached 90.53%.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years